Structural Magnetic Resonance Imaging Research Articles

Less is more: Smaller hippocampal subfield volumes predict greater improvements in posttraumatic stress disorder symptoms over 2 years.

Posttraumatic stress disorder (PTSD) is a heterogeneous disorder, and symptom severity varies over time. Neurobiological factors that predict PTSD symptoms and their chronicity remain unclear. This study investigated whether the volume of the hippocampus and its subfields, particularly cornu ammonis (CA) 1, CA3, and dentate gyrus, are associated with current PTSD symptoms and whether they predict PTSD symptom changes over 2 years. We examined clinical and structural magnetic resonance imaging measures from 252 trauma-exposed post-9/11 veterans (159 with Time 1 PTSD diagnosis) during assessments approximately 2 years apart. Automated hippocampal subfield segmentation was performed with FreeSurfer Version 7.1, producing 19 bilateral subfields. PTSD symptoms were measured at each assessment using the Clinician-Administered PTSD Scale-IV (CAPS). All models included total intracranial volume as a covariate. First, similar to previous reports, we showed that smaller overall hippocampal volume was associated with greater PTSD symptom severity at Time 1. Notably, when examining regions of interest (CA1, CA3, dentate gyrus), we found that smaller Time 1 hippocampal volumes in the bilateral CA1-body and CA2/3-body predicted decreased PTSD symptom severity at Time 2. These findings were not accounted for by combat exposure or treatment history. Additionally, both Time 1 CA1-body and CA2/3-body volume showed unique associations with changes in avoidance/numbing, but not with changes in reexperiencing or hyperarousal symptoms. This supports a more complex and nuanced relationship between hippocampal structure and PTSD symptoms, where during the posttrauma years bigger may not always mean better, and suggests that the CA1-body and CA2/3-body are important factors in the maintenance of PTSD symptoms. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Prediction of Cognitive Progression Due to Alzheimer's Disease in Normal Participants Based on Individual Default Mode Network Metabolic Connectivity Strength

BackgroundPredicting cognitive decline among individuals in the aging population who are already amyloid-β (Aβ) positive or tau positive poses clinical challenges. In Alzheimer's disease research, intra–default mode network (DMN) connections play a pivotal role in diagnosis. In this article, we propose metabolic connectivity within the DMN as a supplementary biomarker to the Aβ, pathological tau, and neurodegeneration framework. MethodsExtracting data from 1292 participants in the Alzheimer’s Disease Neuroimaging Initiative, we collected paired T1-weighted structural magnetic resonance imaging and 18F-labeled-fluorodeoxyglucose positron emission computed tomography scans. Individual metabolic DMN networks were constructed, and metabolic connectivity (MC) strength in the DMN was assessed. In the cognitively unimpaired group, the Cox model identified cognitively unimpaired (MC+), high-risk participants, with Kaplan-Meier survival analyses and hazard ratios revealing the strength of MC’s predictive performance. Spearman correlation analyses explored relationships between MC strength, and Aβ, pathological tau, neurodegeneration biomarkers, and clinical scales. DMN standard uptake value ratio (SUVR) provided comparative insights in the analyses. ResultsBoth MC strength and SUVR exhibited gradual declines with cognitive deterioration, displaying significant intergroup differences. Survival analyses indicated enhanced Aβ and tau prediction with both metrics, with MC strength outperforming SUVR. Combined MC strength and Aβ yielded optimal predictive performance (hazard ratio = 9.29), followed by MC strength and tau (hazard ratio = 8.92). Generally, the strength of MC’s correlations with Aβ, pathological tau, and neurodegeneration biomarkers exceeded SUVR. ConclusionsIndividuals with normal cognition and disrupted DMN metabolic connectivity face an elevated risk of cognitive decline linked to Aβ that precedes metabolic issues.

Identifying gray matter alterations in Cushing's disease using machine learning: An interpretable approach.

Cushing's Disease (CD) is a rare clinical syndrome characterized by excessive secretion of adrenocorticotrophic hormone, leading to significant functional and structural brain alterations as observed in Magnetic Resonance Imaging (MRI). While traditional statistical analysis has been widely employed to investigate these MRI changes in CD, it has lacked the ability to predict individual-level outcomes. To address this problem, this paper has proposed an interpretable machine learning (ML) framework, including model-level assessment, feature-level assessment, and biology-level assessment to ensure a comprehensive analysis based on structural MRI of CD. The ML framework has effectively identified the changes in brain regions in the stage of model-level assessment, verified the effectiveness of these altered brain regions to predict CD from normal controls in the stage of feature-level assessment, and carried out a correlation analysis between altered brain regions and clinical symptoms in the stage of biology-levelassessment. The experimental results of this study have demonstrated that the Insula, Fusiform gyrus, Superior frontal gyrus, Precuneus, and the opercular portion of the Inferior frontal gyrus of CD showed significant alterations in brain regions. Furthermore, our study has revealed significant correlations between clinical symptoms and the frontotemporal lobes, insulin, and olfactory cortex, which also have been confirmed by previousstudies. The ML framework proposed in this study exhibits exceptional potential in uncovering the intricate pathophysiological mechanisms underlying CD, with potential applicability in diagnosing otherdiseases.

Structural MRI analysis of age-related changes and sex differences in marmoset brain volume

The field of aging biology, which aims to extend healthy lifespans and prevent age-related diseases, has turned its focus to the Callithrix jacchus (common marmoset) to understand the aging process better. This study utilized magnetic resonance imaging (MRI) to non-invasively analyze the brains of 216 marmosets, investigating age-related changes in brain structure; the relationship between body weight and brain volume; and potential differences between males and females. The key findings revealed that, similar to humans, Callithrix jacchus experiences a reduction in total intracranial volume, cortex, subcortex, thalamus, and cingulate volumes as they age, highlighting site-dependent changes in brain tissue. Notably, the study also uncovered sex differences in cerebellar volume. These insights into the structural connectivity and volumetric changes in the marmoset brain throughout aging contribute to accumulating valuable knowledge in the field, promising to inform future aging research and interventions for enhancing healthspan.

Brain Functional Connectivity Changes in Rapid Eye Movement Sleep Behavior Disorder

The idiopathic rapid eye movement sleep behavior disorder (iRBD) has been reported as prodromal biomarker for neurodegenerative synucleinopathies. To early detect neurodegeneration in preclinical stage, pathophysiological changes of iRBD have been studied in many ways including structural magnetic resonance imaging (MRI), functional MRI (fMRI), and connectivity analyses. This review summarizes functional connectivity studies using resting-state fMRI in iRBD patients focused on motor-related network, compensatory mechanism of synucleinopathies, cognitive dysfunction, and non-motor symptoms.

Altered perivascular spaces in subcortical white matter in Parkinson’s disease patients with levodopa-induced dyskinesia

Dilated perivascular spaces (PVS) have emerged as a pathological hallmark in various neurological conditions, including Parkinson’s disease (PD). Levodopa-induced dyskinesia (LID), an intractable motor complication of PD, remains enigmatic regarding the distribution patterns of PVS. Our objective was to scrutinize the percent PVS (pPVS) changes within PD patients with LID (PD-LID). In total, 132 individuals were enrolled, including PD-LID (n = 42), PD patients without LID (PD-nLID, n = 45), and healthy controls (HCs, n = 45). Employing an automated approach for PVS quantification based on structural magnetic resonance imaging, we comprehensively evaluated total pPVS in subcortical white matter globally and regionally. A significant increase in global pPVS was observed in PD patients versus HCs, particularly evident in PD-LID relative to HCs. Within the PD-LID group, elevated pPVS was discerned in the right inferior frontal gyrus region (rIFG) (pars opercularis), contrasting with PD-nLID and HCs. Moreover, PD patients exhibited increased pPVS in bilateral superior temporal regions compared to HCs. Notably, pPVS in the rIFG positively correlated with dyskinetic symptoms and could well identify LID. Our findings unveiled PVS alternations in subcortical white matter in PD-LID at both global and regional levels, highlighting the increased pPVS in rIFG as a prospective imaging marker for LID.

Neural correlates of the revised reinforcement sensitivity theory: A cross-sectional structural neuroimaging study in middle-aged adults.

The revised reinforcement sensitivity theory (RST) proposes that neurobiological systems control behavior: the fight-flight-freeze (FFFS) for avoidance of threat; behavioral approach/activation (BAS) for approach to rewards; and behavioral inhibition (BIS) for conflict resolution when avoidance and approach are possible. Neuroimaging studies have confirmed some theoretical associations between brain structures and the BAS and BIS; however, little representative population data are available for the FFFS. We investigated the neural correlates of the revised RST in a sample of 404 middle-aged adults (Mage = 47.18 (SD = 1.38); 54.5% female). Participants underwent structural magnetic resonance imaging and completed health questionnaires and the BIS/BAS/FFFS scales. We used multiple regression analyses to investigate the association between scale scores and volumes of a priori theoretically linked regions of interest while controlling for sex, age, intracranial volume, and cardio-metabolic variables; and conducted exploratory analyses on cortical thickness. The BIS was negatively associated with hippocampus laterality. At standard significance levels, the fear component of the FFFS was positively associated with anterior cingulate cortex; the BAS was positively associated with bilateral caudate; and the BIS was positively associated with posterior cingulate cortex volume. Furthermore, these neurobiological systems showed distinct patterns of association with cortical thickness though future work is needed. Our results showed that the neurobiological systems of the revised RST characterized in rodents can also be identified in the human brain.

Machine Learning Approaches to Identify Affected Brain Regions in Movement Disorders Using MRI Data: A Systematic Review and Diagnostic Meta-analysis.

Movement disorders such as Parkinson's disease are associated with structural and functional changes in specific brain regions. Advanced magnetic resonance imaging (MRI) techniques combined with machine learning (ML) are promising tools for identifying imaging biomarkers and patterns associated with these disorders. We aimed to systematically identify the brain regions most commonly affected in movement disorders using ML approaches applied to structural and functional MRI data. We searched the PubMed and Scopus databases using relevant keywords up to June 2023 for studies that used ML approaches to detect brain regions associated with movement disorders using MRI data. A systematic review and diagnostic meta-analysis. Sixty-seven studies with 6,285 patients were included. Studies utilizing 1.5T or 3T MR scanners and the acquisition of diffusion tensor imaging (DTI), structural MRI (sMRI), functional MRI (fMRI), or a combination of these were included. The authors independently assessed the study quality using the CLAIM and QUADAS-2 criteria and extracted data on diagnostic accuracy measures. Sensitivity, specificity, accuracy, and area under the curve were pooled using random-effects models. Q statistics and the I2 index were used to evaluate heterogeneity, and Begg's funnel plot was used to identify publication bias. sMRI showed the highest sensitivity (93%) and mixed modalities had the highest specificity (90%) for detecting regional abnormalities. sMRI had a 94% sensitivity for identifying subcortical changes. The support vector machine (93%) and logistic regression (91%) models exhibited high diagnostic accuracies. The combination of advanced MR neuroimaging techniques and ML is a promising approach for identifying brain biomarkers and affected regions in movement disorders with subcortical structures frequently implicated. Structural MRI, in particular, showed strong performance. 1 TECHNICAL EFFICACY: Stage 2.

Brain morphometry in former American football players: Findings from the DIAGNOSE CTE research project.

Exposure to repetitive head impacts (RHIs) in contact sports is associated with neurodegenerative disorders including chronic traumatic encephalopathy (CTE) which currently can be diagnosed only at postmortem. American football players are at higher risk of developing CTE given their exposure to RHIs. One promising approach for diagnosing CTE in vivo is to explore known neuropathological abnormalities at postmortem in living individuals using structural magnetic resonance imaging (MRI). MRI brain morphometry was evaluated in 170 male former American football players ages 45-74 years (n = 114 professional; n = 56 college) and 54 same-age unexposed asymptomatic male controls (n = 58 age range 45-74). Cortical thickness and volume of regions of interest were selected based on established CTE pathology findings and were assessed using FreeSurfer. Group differences and interactions with age and exposure factors were evaluated using a generalized least squares model. A separate logistic regression and independent multinomial model were performed to predict each Traumatic Encephalopathy Syndrome (TES) diagnosis core clinical features and provisional level of certainty for CTE pathology using brain regions of interest. Former college and professional American football players (combined) showed significant cortical thickness and/or volume reductions compared to unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula temporal pole and superior frontal gyrus. Post-hoc analyses identified group-level differences between former professional players and unexposed asymptomatic controls in the hippocampus amygdala entorhinal cortex parahippocampal gyrus insula and superior frontal gyrus. Former college players showed significant volume reductions in the hippocampus amygdala and superior frontal gyrus compared to the unexposed asymptomatic controls. We did not observe age-by-group interactions for brain morphometric measures. Interactions between morphometry and exposure measures were limited to a single significant positive association between the age of first exposure to organized tackle football and right insular volume. We found no significant relationship between brain morphometric measures and the TES diagnosis core clinical features and provisional level of certainty for CTE pathology outcomes. These findings suggest that MRI morphometrics detects abnormalities in individuals with a history of RHI exposure that resemble the anatomic distribution of pathological findings from postmortem CTE studies. The lack of findings associating MRI measures with exposure metrics (except for one significant relationship) or TES diagnosis and core clinical features suggests that brain morphometry must be complemented by other types of measures to characterize individuals with RHIs.

Cognitive phenotype and neurodegeneration associated with Tau in Huntington's disease.

The clinical phenotype of Huntington's disease (HD) can be very heterogeneous between patients, even when they share equivalent CAG repeat length, age, or disease burden. This heterogeneity is especially evident in terms of the cognitive profile and related brain changes. To shed light on the mechanisms participating in this heterogeneity, the present study delves into the association between Tau pathology and more severe cognitive phenotypes and brain damage in HD. We used a comprehensive neuropsychological examination to characterize the cognitive phenotype of a sample of 30 participants with early-to-middle HD for which we also obtained 3 T structural magnetic resonance image (MRI) and cerebrospinal fluid (CSF). We quantified CSF levels of neurofilament light chain (NfL), total Tau (tTau), and phosphorylated Tau-231 (pTau-231). Thanks to the cognitive characterization carried out, we subsequently explored the relationship between different levels of biomarkers, the cognitive phenotype, and brain integrity. The results confirmed that more severe forms of cognitive deterioration in HD extend beyond executive dysfunction and affect processes with clear posterior-cortical dependence. This phenotype was in turn associated with higher CSF levels of tTau and pTau-231 and to a more pronounced pattern of posterior-cortical atrophy in specific brain regions closely linked to the cognitive processes affected by Tau. Our findings reinforce the association between Tau pathology, cognition, and neurodegeneration in HD, emphasizing the need to explore the role of Tau in the cognitive heterogeneity of the disease.

Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning.

Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials. The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers. The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity. The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience. Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

Cortical morphology variations during the menstrual cycle in individuals with and without premenstrual dysphoric disorder

BackgroundPremenstrual dysphoric disorder (PMDD) is hypothesized to stem from maladaptive neural sensitivity to ovarian steroid hormone fluctuations. Recently, we found thinner cortices in individuals with PMDD, compared to healthy controls, during the symptomatic phase. Here, we aimed at investigating whether such differences illustrate state-like characteristics specific to the symptomatic phase, or trait-like features defining PMDD. MethodsPatients and controls were scanned using structural magnetic resonance imaging during the mid-follicular and late-luteal phase of the menstrual cycle. Group-by-phase interaction effects on cortical architecture metrics (cortical thickness, gyrification index, cortical complexity, and sulcal depth) were assessed using surface-based morphometry. ResultsIndependently of menstrual cycle phase, a main effect of diagnostic group on surface metrics was found, primarily illustrating thinner cortices (0.3 < Cohen's d > 1.1) and lower gyrification indices (0.4 < Cohen's d > 1.0) in patients compared to controls. Furthermore, menstrual cycle-specific effects were detected across all participants, depicting a decrease in cortical thickness (0.4 < Cohen's d > 1.7) and region-dependent changes in cortical folding metrics (0.4 < Cohen's d > 2.2) from the mid-follicular to the late luteal phase. LimitationsSmall effects (d = 0.3) require a larger sample size to be accurately characterized. ConclusionsThese findings provide initial evidence of trait-like cortical characteristics of the brain of individuals with premenstrual dysphoric disorder, together with indications of menstrual cycle-related variations in cortical architecture in patients and controls. Further investigations exploring whether these differences constitute stable vulnerability markers or develop over the years may help understand PMDD etiology.

Magnetic Resonance Imaging (MRI) Insights into Brain Morphology and Connectivity Disruptions in Schizophrenia

This review explores the transformative impact of Magnetic Resonance Imaging (MRI) on understanding schizophrenia. Structural MRI has unveiled significant alterations in brain regions like the hippocampus and prefrontal cortex, offering detailed insights into anatomical irregularities. Functional MRI studies have shed light on disruptions within the default mode network, providing valuable insights into cognitive deficits associated with the disorder. Additionally, Diffusion Tensor Imaging has highlighted white matter abnormalities, underscoring compromised interregional communication within the brain. Despite persistent challenges such as population heterogeneity and variations in imaging protocols, MRI techniques have significantly advanced our understanding of schizophrenia's neurobiological aspects.

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion magnetic resonance imaging [MRI]). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N = 34,029) and ABCD Study (N = 8607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI in a genome-wide association study (GWAS) with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.

Impaired olfactory identification in dementia-free individuals is associated with the functional abnormality of the precuneus

ObjectiveOlfactory dysfunction indicates a higher risk of developing dementia. However, the potential structural and functional changes are still largely unknown. MethodsA total of 236 participants were enrolled, including 45 Alzheimer's disease (AD) individuals and 191dementia-free individuals. Detailed study methods, comprising neuropsychological assessment and olfactory identification test (University of Pennsylvania smell identification test, UPSIT), as well as structural and functional magnetic resonance imaging (MRI) were applied in this research. The dementia-free individuals were divided into two sub-groups based on olfactory score: dementia-free with olfactory dysfunction (DF-OD) sub-group and dementia-free without olfactory dysfunction (DF-NOD) sub-group. The results were analyzed for subsequent intergroup comparisons and correlations. The cognitive assessment was conducted again three years later. Results(i) At dementia-free stage, there was a positive correlation between olfactory score and cognitive function. (ii) In dementia-free group, the volume of crucial brain structures involved in olfactory recognition and processing (such as amygdala, entorhinal cortex and basal forebrain volumes) are positively associated with olfactory score. (iii) Compared to the DF-NOD group, the DF-OD group showed a significant reduction in olfactory network (ON) function. (iv) Compared to DF-NOD group, there were significant functional connectivity (FC) decline between PCun_L(R)_4_1 in the precuneus of posterior default mode network (pDMN) and the salience network (SN) in DF-OD group, and the FC values decreased with falling olfactory scores. Moreover, in DF-OD group, the noteworthy reduction in FC were observed between PCun_L(R)_4_1 and amygdala, which was a crucial component of ON. (v) The AD conversion rate of DF-OD was 29.41%, while the DF-NOD group was 12.50%. The structural and functional changes in the precuneus were also observed in AD and were more severe. ConclusionsIn addition to the olfactory circuit, the precuneus is a critical structure in the odor identification process, whose abnormal function underlies the olfactory identification impairment of dementia-free individuals.

Menarche, pubertal timing and the brain: female-specific patterns of brain maturation beyond age-related development

BackgroundPuberty depicts a period of profound and multifactorial changes ranging from social to biological factors. While brain development in youths has been studied mostly from an age perspective, recent evidence suggests that pubertal measures may be more sensitive to study adolescent neurodevelopment, however, studies on pubertal timing in relation to brain development are still scarce.MethodsWe investigated if pre- vs. post-menarche status can be classified using machine learning on cortical and subcortical structural magnetic resonance imaging (MRI) data from strictly age-matched adolescent females from the Adolescent Brain Cognitive Development (ABCD) cohort. For comparison of the identified menarche-related patterns to age-related patterns of neurodevelopment, we trained a brain age prediction model on data from the Philadelphia Neurodevelopmental Cohort and applied it to the same ABCD data, yielding differences between predicted and chronological age referred to as brain age gaps. We tested the sensitivity of both these frameworks to measures of pubertal maturation, specifically age at menarche and puberty status.ResultsThe machine learning model achieved moderate but statistically significant accuracy in the menarche classification task, yielding for each subject a class probability ranging from 0 (pre-) to 1 (post- menarche). Comparison to brain age predictions revealed shared and distinct patterns of neurodevelopment captured by both approaches. Continuous menarche class probabilities were positively associated with brain age gaps, but only the menarche class probabilities—not the brain age gaps—were associated with age at menarche.ConclusionsThis study demonstrates the use of a machine learning model to classify menarche status from structural MRI data while accounting for age-related neurodevelopment. Given its sensitivity towards measures of puberty timing, our work suggests that menarche class probabilities may be developed toward an objective brain-based marker of pubertal development.

Functional organization of the human corpus callosum unveiled with BOLD-fMRI gradients

Abstract Gradients capture the underlying functional organization of the brain. Cortical gradients have been well characterized, however very little is known about the underlying gradient of the white matter. Here, we proposed a functionally gradient mapping of the corpus callosum by using blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD-fMRI), which for the first time uncovered three distinct but stable spatial axes: posterior-anterior, dorsal-ventral, and left-right. The three spatial patterns were replicated in another independent cohort and robust across scanning conditions. We further associated the three gradient maps with brain anatomy, connectome, and task-related brain functions, by using structural magnetic resonance imaging, both resting-state and task fMRI, and diffusion tensor imaging data. The posterior-anterior gradient distribution of the corpus callosum showed a similar pattern with the cerebral cortex, gradually extending from the primary cortex to the transmodal cortex. The dorsal-ventral gradient distribution revealed an N-shaped pattern from the primary cortex to the higher-order cognitive cortex. The posterior-anterior and dorsal-ventral gradient maps were also associated with white-matter microstructures, such as fractional anisotropy and myelin water fraction. The left-right gradient showed an inverted V-shaped pattern, which delineated the inter-hemisphere separation. These findings provide fundamental insight into the functional organization of the human corpus callosum, unveiling potential patterns of functional interaction with the cerebral cortex and their associations with cognitive behaviors.

Structural MRI measures are associated with fatigue severity and persistence in a large, real-world cohort of people with multiple sclerosis

Background: Results of research on radiological hallmarks of multiple sclerosis (MS) fatigue have been conflicting. Objective: To investigate the associations of lesion and brain compartment volumes with fatigue severity and persistence in people with multiple sclerosis (PwMS). Methods: The Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network collects standardized data during routine care of PwMS from 10 healthcare institutions. Magnetic resonance imaging (MRI) predictors included baseline brain parenchymal (BPF) and gray matter fractions (GMF) and T2 lesion volume (T2LV). The Quality of Life in Neurological Disorders (Neuro-QOL) fatigue subscore was analyzed linearly and categorically using T-score cutpoints, with a period of elevated symptoms defined as T-score ⩾ mean + 0.5 SD over follow-up. Results: At baseline, of 4012 participants (average age: 45.6 ± 11.8 years; 73% female; 31% progressive MS), 2058 (51%) had no fatigue, 629 (16%) had mild fatigue, and 1325 (33%) had moderate-to-severe fatigue. One SD greater baseline BPF and GMF were associated with 0.83 (p < 0.001) and 0.38 (p = 0.02) lower values in the baseline Neuro-QOL fatigue T-score. A 1 SD lower log of total T2LV was associated with a 0.49 (p < 0.001) lower baseline fatigue T-score. Higher BPF and lower T2LV at baseline were associated with lower odds of subsequent periods of elevated fatigue. Conclusion: Baseline lesion burden and lower generalized whole-brain volumes were associated with MS fatigue in cross-sectional and longitudinal analyses in a large, real-world cohort of PwMS.

Prediction of psychotic disorder in individuals with clinical high-risk state by multimodal machine-learning: A preliminary study

Objective markers which can reliably predict psychosis transition among individuals with at-risk mental state (ARMS) are warranted. In this study, sixty-five ARMS subjects [of whom 17 (26.2%) later developed psychosis] were recruited, and we performed supervised linear support vector machine (SVM) with a variety of combinations of.modalities (clinical features, cognition, structural magnetic resonance imaging, eventrelated.potentials, and polyunsaturated fatty acids) to predict future psychosis onset. While single-modality SVMs showed a poor to fair accuracy, multi-modal SVMs revealed better predictions, up to 0.88 of the balanced accuracy, suggesting the advantage of multi-modal machine-learning methods for forecasting psychosis onset in ARMS.

Randomized controlled double-blind trial of methylprednisolone versus placebo in patients with post-COVID-19 syndrome and cognitive deficits: study protocol of the post-corona-virus immune treatment (PoCoVIT) trial

IntroductionPost-COVID-19 Syndrome (PCS) includes neurological manifestations, especially fatigue and cognitive deficits. Immune dysregulation, autoimmunity, endothelial dysfunction, viral persistence, and viral reactivation are discussed as potential pathophysiological mechanisms. The post-corona-virus immune treatment (PoCoVIT) trial is a phase 2a randomized, controlled, double-blind trial designed to evaluate the effect of methylprednisolone versus placebo on cognitive impairment in PCS. This trial is designed based on the hypothesised autoimmunological pathogenesis and positive aberrations, employing a series of off-label applications.MethodsRecruitment criteria include a diagnosis of PCS, a minimum age of 18 years and self-reported cognitive deficits at screening. A total of 418 participants will be randomly assigned to either verum or placebo intervention in the first phase of the trial. The trial will consist of a first trial phase intervention with methylprednisolone versus placebo for six weeks, followed by a six-week treatment interruption period. Subsequently, an open second phase will offer methylprednisolone to all participants for six weeks. Outpatient follow-up visits will take place two weeks after each trial medication cessation. The third and final follow-up, at week 52, will be conducted through a telephone interview. The primary outcome measures an intra-patient change of 15 or more points in the memory satisfaction subscale of the Multifactorial Memory Questionnaire (MMQ) from baseline to follow-up 1 (week 8). Key secondary outcomes include long-term intra-patient changes in memory satisfaction from baseline to follow-up 2 (week 20), changes in other MMQ subscales (follow-up 1 and 2), and changes in neuropsychological and cognitive scores, along with assessments through questionnaires focusing on quality of life, fatigue, and mood over the same periods. Exploratory outcomes involve molecular biomarkers variations in serum and cerebrospinal fluid, as well as structural and functional brain magnetic resonance imaging (MRI) parameters changes related to cognition.PerspectiveThis trial aims to contribute novel evidence for treating patients with PCS, with a primary focus on those manifesting cognitive deficits. By doing so, it may enhance comprehension of the underlying pathophysiological mechanisms, thereby facilitating biomarker research to advance our understanding and treatment of patients with PCS.