Gray Matter Research Articles

Obsessive–compulsive Disorder in a Young Girl with Subependymal Heterotopia, Aqueductal Stenosis, and Mega Cisterna Magna Along with Left-sided Mild Cerebellar Hypoplasia

Gray matter heterotopia (GMH) is a rare disorder in the general population, but recent attention to its psychiatric aspects encouraged us to introduce a patient with a form of gray matter heterotopia, who suffers from the anatomical abnormalities with a variety of psychiatric disorders. During the late teenage years or early adulthood, a wide variety of neuropsychiatric symptoms may be present, which can lead to diagnostic difficulties. The Dandy–Walker variant is a milder form of the Dandy–Walker complex and is characterized by normal-sized posterior fossa, mild vermian hypoplasia, and a cystic lesion that communicates with the fourth ventricle. This syndrome has been described in association with schizophrenia, obsessive–compulsive disorder (OCD), manic episode, psychosis, and recurrent catatonia. Presenting a case report of an 11-year-old girl with OCD who was accidentally detected with the presence of GMH along with aqueductal stenosis, mega cisterna magna, and left-sided mild cerebellar hypoplasia.

Behavioral and brain morphological changes before and after hemispherotomy.

Hemispherotomy is an effective surgery for treating refractory epilepsy from diffuse unihemispheric lesions. To date, postsurgery neuroplastic changes supporting behavioral recovery after left or right hemispherotomy remain unclear. In the present study, we systematically investigated changes in gray matter volume (GMV) before and after surgery and further analyzed their relationships with behavioral scores in two large groups of pediatric patients with left and right hemispherotomy (29 left and 28 right). To control for the dramatic developmental effect during this stage, age-adjusted GMV within unaffected brain regions was derived voxel by voxel using a normative modeling approach with an age-matched reference cohort of 2115 healthy children. Widespread GMV increases in the contralateral cerebrum and ipsilateral cerebellum and GMV decreases in the contralateral cerebellum were consistently observed in both patient groups, but only the left hemispherotomy patients showed GMV decreases in the contralateral cingulate gyrus. Intriguingly, the GMV decrease in the contralateral cerebellum was significantly correlated with improvement in behavioral scores in the right but not the left hemispherotomy patients. Importantly, the preoperative voxelwise GMV features can be used to significantly predict postoperative behavioral scores in both patient groups. These findings indicate an important role of the contralateral cerebellum in the behavioral recovery following right hemispherotomy and highlight the predictive potential of preoperative imaging features in postoperative behavioral performance.

Dysphagia in primary progressive aphasia: Clinical predictors and neuroanatomical basis.

Dysphagia is an important feature of neurodegenerative diseases and potentially life-threatening in primary progressive aphasia (PPA) but remains poorly characterized in these syndromes. We hypothesized that dysphagia would be more prevalent in nonfluent/agrammatic variant (nfv)PPA than other PPA syndromes, predicted by accompanying motor features, and associated with atrophy affecting regions implicated in swallowing control. In a retrospective case-control study at our tertiary referral centre, we recruited 56 patients with PPA (21 nfvPPA, 22 semantic variant [sv]PPA, 13 logopenic variant [lv]PPA). Using a pro forma based on caregiver surveys and clinical records, we documented dysphagia (present/absent) and associated, potentially predictive clinical, cognitive, and behavioural features. These were used to train a machine learning model. Patients' brain magnetic resonance imaging scans were assessed using voxel-based morphometry and region-of-interest analyses comparing differential atrophy profiles associated with dysphagia presence/absence. Dysphagia was significantly more prevalent in nfvPPA (43% vs. 5% svPPA and no lvPPA). The machine learning model revealed a hierarchy of features predicting dysphagia in the nfvPPA group, with excellent classification accuracy (90.5%, 95% confidence interval = 77.9-100); the strongest predictor was orofacial apraxia, followed by older age, parkinsonism, more severe behavioural disturbance, and more severe cognitive impairment. Significant grey matter atrophy correlates of dysphagia in nfvPPA were identified in left middle frontal, right superior frontal, and right supramarginal gyri and right caudate. Dysphagia is a common feature of nfvPPA, linked to underlying corticosubcortical network dysfunction. Clinicians should anticipate this symptom particularly in the context of other motor features and more severe disease.

Analysis of white matter hyperintensities in Alzheimer’s disease and vascular dementia with magnetic resonance imaging

Objectives: The study aimed to analyze the brain white matter hyperintensities (WMHs) of patients with vascular dementia (VaD) and Alzheimer’s disease (AD) using magnetic resonance imaging to determine whether white matter lesions in the brain could be detected by a computer using image processing methods. Patients and methods: In this retrospective observational study, a unimodal, unsupervised, and automatic method was developed, and magnetic resonance imaging of 35 patients were examined. Of the 35 patients, 19 (14 males, 5 females; mean age: 73.2±6.7 years; range, 56 to 83 years) were picked from patients with AD or VaD who were admitted to a neurology clinic between January 2016 and December 2022 (Group 1). The remaining 16 patients (10 females, 5 males; mean age: 80.4±5.4 years; range, 69 to 92 years) were included from the ABVIB (Aging Brain: Vasculature, Ischemia, and Behavior) study from the ADNI (Alzheimer’s Disease Neuroimaging Initiative) database (Group 2). To calculate the volume of WMHs, a detailed analysis was conducted. Initially, skull stripping was performed, and then the brain was segmented. Afterward, two types of masks (Mask1 and Mask-2) were obtained by applying painting, decreasing, and blurring processes to the segmented white matter. These masks limited the region that was searched for WMHs. With this limitation, false positives that could arise from gray matter intensities were tried to be prevented. To evaluate the accuracy of WMH detection, a user interface was developed, and manual marking was conducted by an expert neurologist. After WMH detection, WMH volumes were calculated. Results: For Group 1, the similarity index was found to be 0.76 for Mask-1 and 0.80 for Mask-2, while for Group 2, the similarity index was 0.71 for Mask-1 and 0.87 for Mask-2. In patients with AD, the mean WMH lesion load (LL) was 15.16±16.59 mL. In patients with VaD, who were expected to suffer more from WMHs, the mean WMH LL was 29.22±11.40 mL. In Group 2, the mean WMH LL was 17.77±12.26 mL. Conclusion: This study may contribute to the literature since it is an automatic, unimodal, and unsupervised method that was applied to both a completely unique data set with many different scanning parameters and an open database data set.

Gray matter volume differences in intimate partner violence perpetrators and its role in explaining dropout and recidivism

AimPsychological instruments that are employed to adequately explain treatment compliance and recidivism of intimate partner violence (IPV) perpetrators present a limited ability and certain biases. Therefore, it becomes necessary to incorporate new techniques, such as magnetic resonance imaging (MRI), to be able to surpass those limitations and measure central nervous system characteristics to explain dropout (premature abandonment of intervention) and recidivism. MethodThe main objectives of this study were: 1) to assess whether IPV perpetrators (n=60) showed differences in terms of their brain’s regional gray matter volume (GMV) when compared to a control group of non-violent men (n=57); 2) to analyze whether the regional GMV of IPV perpetrators before starting a tailored intervention program explain treatment compliance (dropout) and recidivism rate. ResultsIPV perpetrators presented increased GMV in the cerebellum and the occipital, temporal, and subcortical brain regions compared to controls. There were also bilateral differences in the occipital pole and subcortical structures (thalamus, and putamen), with IPV perpetrators presenting reduced GMV in the above-mentioned brain regions compared to controls. Moreover, while a reduced GMV of the left pallidum explained dropout, a considerable number of frontal, temporal, parietal, occipital, subcortical and limbic regions added to dropout to explain recidivism. ConclusionsOur study found that certain brain structures not only distinguished IPV perpetrators from controls but also played a role in explaining dropout and recidivism. Given the multifactorial nature of IPV perpetration, it is crucial to combine neuroimaging techniques with other psychological instruments to effectively create risk profiles of IPV perpetrators.

Covariation of Skull and Brain Morphology in Domestic Dogs.

Despite their distinct embryonic origins, the skull and brain are highly integrated. Understanding the covariation between the skull and brain can shed light on anatomical, cognitive, and behavioral traits in extant and extinct species. Domestic dogs offer a unique opportunity to investigate skull-brain covariation due to their diverse skull morphologies and neural anatomy. To assess this question, we examined T2-weighted MRI studies of 62 dogs from 33 breeds, plus an additional 17 dogs of mixed or unknown breeds. Scans were opportunistically collected from a veterinary teaching hospital of dogs that were referred for neurological examination but did not have grossly observable structural brain abnormalities. As the neurocrania of dogs become broader and shorter, there is a significant decrease in the gray matter volume of the right olfactory bulb, frontal cortex, marginal gyrus, and cerebellum. On the other hand, as the neurocrania of dogs become narrower and longer, there is a significant decrease in the gray matter volume of the olfactory bulb, frontal cortex, temporal cortex, amygdala, hypothalamus, hippocampus, periaqueductal gray, cerebellum, and brainstem. Selective breeding for specific skull shapes may impact canine brain anatomy and function.

Disturbances of thalamus and prefrontal cortex contribute to cognitive aging: A structure-function coupling analysis based on KL divergence

Normal aging is accompanied by changes in brain structure and function associated with cognitive decline. Structural and functional abnormalities, particularly the prefrontal cortex (PFC) and subcortical regions, contributed to cognitive aging. However, it remains unclear how the synchronized changes in structure and function of individual brain regions affect the cognition in aging. Using 3D T1-weighted structural data and movie watching functional magnetic resonance imaging data in a sample of 422 healthy individuals (ages from 18 to 87 years), we constructed regional structure–function coupling (SFC) of cortical and subcortical regions by quantifying the distribution similarity of gray matter volume (GMV) and amplitude of low-frequency fluctuation (ALFF). Further, we investigated age-related changes in SFC and its relationship with cognition. With aging, increased SFC localized in PFC, thalamus and caudate nucleus, decreased SFC in temporal cortex, lateral occipital cortex and putamen. Moreover, the SFC in the PFC was associated with executive function and thalamus was associated with the fluid intelligence, and partially mediated age-related cognitive decline. Collectively, our results highlight that tighter structure–function synchron of the PFC and thalamus might contribute to age-related cognitive decline, and provide insight into the substrate of the thalamo-prefrontal pathway with cognitive aging.

Baseline multimodal imaging to predict longitudinal clinical decline in atypical Alzheimer's disease

There are recognized neuroimaging regions of interest in typical Alzheimer's disease which have been used to track disease progression and aid prognostication. However, there is a need for validated baseline imaging markers to predict clinical decline in atypical Alzheimer's Disease. We aimed to address this need by producing models from baseline imaging features using penalized regression and evaluating their predictive performance on various clinical measures.Baseline multimodal imaging data, in combination with clinical testing data at two time points from 46 atypical Alzheimer's Disease patients with a diagnosis of logopenic progressive aphasia (N = 24) or posterior cortical atrophy (N = 22), were used to generate our models. An additional 15 patients (logopenic progressive aphasia = 7, posterior cortical atrophy = 8), whose data were not used in our original analysis, were used to test our models. Patients underwent MRI, FDG-PET and Tau-PET imaging and a full neurologic battery at two time points. The Schaefer functional atlas was used to extract network-based and regional gray matter volume or PET SUVR values from baseline imaging. Penalized regression (Elastic Net) was used to create models to predict scores on testing at Time 2 while controlling for baseline performance, education, age, and sex. In addition, we created models using clinical or Meta Region of Interested (ROI) data to serve as comparisons.We found the degree of baseline involvement on neuroimaging was predictive of future performance on cognitive testing while controlling for the above measures on all three imaging modalities. In many cases, model predictability improved with the addition of network-based neuroimaging data to clinical data. We also found our network-based models performed superiorly to the comparison models comprised of only clinical or a Meta ROI score.Creating predictive models from imaging studies at a baseline time point that are agnostic to clinical diagnosis as we have described could prove invaluable in both the clinical and research setting, particularly in the development and implementation of future disease modifying therapies.

Structural and functional connectivity in relation to executive functions in antipsychotic-naïve patients with first episode schizophrenia

Patients with schizophrenia exhibit structural and functional dysconnectivity but the relationship to the well-documented cognitive impairments is less clear. This study investigates associations between structural and functional connectivity and executive functions in antipsychotic-naïve patients experiencing schizophrenia. Sixty-four patients with schizophrenia and 95 matched controls underwent cognitive testing, diffusion weighted imaging and resting state functional magnetic resonance imaging. In the primary analyses, groupwise interactions between structural connectivity as measured by fixel-based analyses and executive functions were investigated using multivariate linear regression analyses. For significant structural connections, secondary analyses examined whether functional connectivity and associations with executive functions also differed for the two groups. In group comparisons, patients exhibited cognitive impairments across all executive functions compared to controls (p < 0.001), but no group difference were observed in the fixel-based measures. Primary analyses revealed a groupwise interaction between planning abilities and fixel-based measures in the left anterior thalamic radiation (p = 0.004), as well as interactions between cognitive flexibility and fixel-based measures in the isthmus of corpus callosum and cingulum (p = 0.049). Secondary analyses revealed increased functional connectivity between grey matter regions connected by the left anterior thalamic radiation (left thalamus with pars opercularis p = 0.018, and pars orbitalis p = 0.003) in patients compared to controls. Moreover, a groupwise interaction was observed between cognitive flexibility and functional connectivity between contralateral regions connected by the isthmus (precuneus p = 0.028, postcentral p = 0.012), all p-values corrected for multiple comparisons. We conclude that structural and functional connectivity appear to associate with executive functions differently in antipsychotic-naïve patients with schizophrenia compared to controls.

Detailed delineation of the fetal brain in diffusion MRI via multi-task learning.

Diffusion-weighted MRI is increasingly used to study the normal and abnormal development of fetal brain inutero. Recent studies have shown that dMRI can offer invaluable insights into the neurodevelopmental processes in the fetal stage. However, because of the low data quality and rapid brain development, reliable analysis of fetal dMRI data requires dedicated computational methods that are currently unavailable. The lack of automated methods for fast, accurate, and reproducible data analysis has seriously limited our ability to tap the potential of fetal brain dMRI for medical and scientific applications. In this work, we developed and validated a unified computational framework to (1) segment the brain tissue into white matter, cortical/subcortical gray matter, and cerebrospinal fluid, (2) segment 31 distinct white matter tracts, and (3) parcellate the brain's cortex and delineate the deep gray nuclei and white matter structures into 96 anatomically meaningful regions. We utilized a set of manual, semi-automatic, and automatic approaches to annotate 97 fetal brains. Using these labels, we developed and validated a multi-task deep learning method to perform the three computations. Our evaluations show that the new method can accurately carry out all three tasks, achieving a mean Dice similarity coefficient of 0.865 on tissue segmentation, 0.825 on white matter tract segmentation, and 0.819 on parcellation. The proposed method can greatly advance the field of fetal neuroimaging as it can lead to substantial improvements in fetal brain tractography, tract-specific analysis, and structural connectivity assessment.

The uniqueness of human vulnerability to brain aging in great ape evolution.

Aging is associated with progressive gray matter loss in the brain. This spatially specific, morphological change over the life span in humans is also found in chimpanzees, and the comparison between these great ape species provides a unique evolutionary perspective on human brain aging. Here, we present a data-driven, comparative framework to explore the relationship between gray matter atrophy with age and recent cerebral expansion in the phylogeny of chimpanzees and humans. In humans, we show a positive relationship between cerebral aging and cortical expansion, whereas no such relationship was found in chimpanzees. This human-specific association between strong aging effects and large relative cortical expansion is particularly present in higher-order cognitive regions of the ventral prefrontal cortex and supports the "last-in-first-out" hypothesis for brain maturation in recent evolutionary development of human faculties.

Synergistic associations of CD33 variants and hypertension with brain and cognitive aging among dementia-free older adults: A population-based study.

CD33 rs3865444 and hypertension (HTN) are related to cognitive impairment, individually. However, little is known about their combined effects on cognitive function in older adults. This population-based study included 4368 dementia-free participants (age ≥65 years) in the Multimodal Interventions to Delay Dementia and Disability in Rural China (MIND-China), with data available in 1044 persons for gray matter volume and 85 persons for cerebral blood flow (CBF). We used general linear regression and mediation models to examine the associations of rs3865444 and HTN with cognition, brain atrophy, and CBF. Among rs3865444 CC carriers, HTN and late-life HTN were significantly associated with impaired cognition. Midlife and late-life HTN were correlated with brain atrophy. CD33 rs3865444 CC moderated the mediation effect of gray matter volume on the association between HTN and global cognition. HTN was correlated with low CBF in rs3865444 CC carriers. There are synergistic associations of CD33 rs3865444 and HTN with brain and cognitive aging in dementia-free older adults.

Changed ventral striatum structural covariance and grey matter volume in depression during a one-year follow-up

Empirical findings suggest reduced cortico-striatal structural connectivity in patients with major depressive disorder (MDD). However, the relationship between the abnormal structural covariance and one-year outcome of first-episode drug-naive patients has not been evaluated. This longitudinal study aimed to identify specific changes of ventral striatum-related brain structural covariance and grey matter volume in forty-two first-episode patients with major depression disorder compared with thirty-seven healthy controls at the baseline and the one-year follow-up conditions. At the baseline, patients showed decreased structural covariance between the left ventral striatum and the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus (MFG), right supplementary motor area (SMA) and left precentral gyrus and increased grey matter volume at the left fusiform and left parahippocampus. At the one-year follow-up, patients showed decreased structural covariance between the left ventral striatum and the right SFG, right MFG, left precentral gyrus and left postcentral gyrus, and increased structural covariance between the right ventral striatum and the right amygdala, right hippocampus, right parahippocampus, right superior temporal pole, right insula and right olfactory bulb and decreased volume at the left SMA compared with controls. These findings suggest that specific ventral striatum connectivity changes contribute to the early brain development of the MDD.

Neurostructural, Neurofunctional, and Clinical Features of Chronic, Untreated Schizophrenia: A Narrative Review.

Studies of individuals with chronic, untreated schizophrenia (CUS) can provide important insights into the natural course of schizophrenia and how antipsychotic pharmacotherapy affects neurobiological aspects of illness course and progression. We systematically review 17 studies on the neuroimaging, cognitive, and epidemiological aspects of CUS individuals. These studies were conducted at the Shanghai Mental Health Center, Institute of Mental Health at Peking University, and Huaxi MR Research Center between 2013 and 2021. CUS is associated with cognitive impairment, severe symptoms, and specific demographic characteristics and is different significantly from those observed in antipsychotic-treated individuals. Furthermore, CUS individuals have neurostructural and neurofunctional alterations in frontal and temporal regions, corpus callosum, subcortical, and visual processing areas, as well as default-mode and somatomotor networks. As the disease progresses, significant structural deteriorations occur, such as accelerated cortical thinning in frontal and temporal lobes, greater reduction in fractional anisotropy in the genu of corpus callosum, and decline in nodal metrics of gray mater network in thalamus, correlating with worsening cognitive deficits and clinical outcomes. In addition, striatal hypertrophy also occurs, independent of antipsychotic treatment. Contrasting with the negative neurostructural and neurofunctional effects of short-term antipsychotic treatment, long-term therapy frequently results in significant improvements. It notably enhances white matter integrity and the functions of key subcortical regions such as the amygdala, hippocampus, and striatum, potentially improving cognitive functions. This narrative review highlights the progressive neurobiological sequelae of CUS, the importance of early detection, and long-term treatment of schizophrenia, particularly because treatment may attenuate neurobiological deterioration and improve clinical outcomes.

Schizophrenia and Neurodevelopment: Insights From Connectome Perspective.

Schizophrenia is conceptualized as a brain connectome disorder that can emerge as early as late childhood and adolescence. However, the underlying neurodevelopmental basis remains unclear. Recent interest has grown in children and adolescent patients who experience symptom onset during critical brain development periods. Inspired by advanced methodological theories and large patient cohorts, Chinese researchers have made significant original contributions to understanding altered brain connectome development in early-onset schizophrenia (EOS). We conducted a search of PubMed and Web of Science for studies on brain connectomes in schizophrenia and neurodevelopment. In this selective review, we first address the latest theories of brain structural and functional development. Subsequently, we synthesize Chinese findings regarding mechanisms of brain structural and functional abnormalities in EOS. Finally, we highlight several pivotal challenges and issues in this field. Typical neurodevelopment follows a trajectory characterized by gray matter volume pruning, enhanced structural and functional connectivity, improved structural connectome efficiency, and differentiated modules in the functional connectome during late childhood and adolescence. Conversely, EOS deviates with excessive gray matter volume decline, cortical thinning, reduced information processing efficiency in the structural brain network, and dysregulated maturation of the functional brain network. Additionally, common functional connectome disruptions of default mode regions were found in early- and adult-onset patients. Chinese research on brain connectomes of EOS provides crucial evidence for understanding pathological mechanisms. Further studies, utilizing standardized analyses based on large-sample multicenter datasets, have the potential to offer objective markers for early intervention and disease treatment.

Improving 18F-FDG PET Quantification Through a Spatial Normalization Method.

Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

Secondary analysis reveals gut microbiota differences in patients with Parkinson’s disease and/or cognitive impairment

Background: Parkinson’s disease (PD) is a neurodegenerative disorder, and the main clinical characteristics are bradykinesia and muscle stiffness. Cognitive impairment (CI) is a prevalent non-motor manifestation observed in individuals with PD. According to disease severity, it can be divided into PD with mild cognitive impairment (MCI) and PD dementia. CI in PD patients may precede motor symptoms, and the gut microbiota plays an important role in PD pathogenesis. Therefore, gut microbiota may be one of the diagnostic targets for PD-CI. Methods: This study compared the gut microbiota of 43 PD-CI patients [Montreal Cognitive Assessment (MoCA) score &lt; 26] and 38 PD patients without CI (MoCA ≥ 26). Patients’ neuropsychological conditions, depression scale, and brain structure scanned by magnetic resonance imaging (MRI) were also recorded. The fecal metagenomic datasets of patients with PD, PD-CI, and CI only were retrieved from public databases for reanalysis to explore the relationship between PD, CI, and gut microbiota. Results: We found that the cortical thickness and the volume of the hippocampus, gray matter, and thalamus were significantly reduced among patients with PD-CI compared to PD without CI (P &lt; 0.05). Moreover, the gut microbiome in patients with PD-CI had fewer short-chain fatty acid (SCFA) producing bacteria and more pathogenic bacteria. There were also alterations in patterns of metabolic pathway-encoding genes. Additionally, PD affected gut microbiota more than CI. Conclusion: CI may aggravate the severity of PD, but it did not drastically alter subjects’ gut microbiota. This study reveals the relationship between gut microbiota, PD, and CI.

A deep phenotyping study in mouse and iPSC models to understand the role of oligodendroglia in optic neuropathy in Wolfram syndrome

Wolfram syndrome (WS) is a rare childhood disease characterized by diabetes mellitus, diabetes insipidus, blindness, deafness, neurodegeneration and eventually early death, due to autosomal recessive mutations in the WFS1 (and WFS2) gene. While it is categorized as a neurodegenerative disease, it is increasingly becoming clear that other cell types besides neurons may be affected and contribute to the pathogenesis. MRI studies in patients and phenotyping studies in WS rodent models indicate white matter/myelin loss, implicating a role for oligodendroglia in WS-associated neurodegeneration. In this study, we sought to determine if oligodendroglia are affected in WS and whether their dysfunction may be the primary cause of the observed optic neuropathy and brain neurodegeneration. We demonstrate that 7.5-month-old Wfs1∆exon8 mice display signs of abnormal myelination and a reduced number of oligodendrocyte precursor cells (OPCs) as well as abnormal axonal conduction in the optic nerve. An MRI study of the brain furthermore revealed grey and white matter loss in the cerebellum, brainstem, and superior colliculus, as is seen in WS patients. To further dissect the role of oligodendroglia in WS, we performed a transcriptomics study of WS patient iPSC-derived OPCs and pre-myelinating oligodendrocytes. Transcriptional changes compared to isogenic control cells were found for genes with a role in ER function. However, a deep phenotyping study of these WS patient iPSC-derived oligodendroglia unveiled normal differentiation, mitochondria-associated endoplasmic reticulum (ER) membrane interactions and mitochondrial function, and no overt signs of ER stress. Overall, the current study indicates that oligodendroglia functions are largely preserved in the WS mouse and patient iPSC-derived models used in this study. These findings do not support a major defect in oligodendroglia function as the primary cause of WS, and warrant further investigation of neurons and neuron-oligodendroglia interactions as a target for future neuroprotective or -restorative treatments for WS.

Nuclear-Magnetic-Resonance-Spectroscopy-Derived Serum Biomarkers of Metabolic Vulnerability Are Associated with Disability and Neurodegeneration in Multiple Sclerosis.

Metabolic vulnerabilities can exacerbate inflammatory injury and inhibit repair in multiple sclerosis (MS). The purpose was to evaluate whether blood biomarkers of inflammatory and metabolic vulnerability are associated with MS disability and neurodegeneration. Proton nuclear magnetic resonance spectra were obtained from serum samples from 153 healthy controls, 187 relapsing-remitting, and 91 progressive MS patients. The spectra were analyzed to obtain concentrations of lipoprotein sub-classes, glycated acute-phase proteins, and small-molecule metabolites, including leucine, valine, isoleucine, alanine, and citrate. Composite indices for inflammatory vulnerability, metabolic malnutrition, and metabolic vulnerability were computed. MS disability was measured on the Expanded Disability Status Scale. MRI measures of lesions and whole-brain and tissue-specific volumes were acquired. Valine, leucine, isoleucine, alanine, the Inflammatory Vulnerability Index, the Metabolic Malnutrition Index, and the Metabolic Vulnerability Index differed between healthy control and MS groups in regression analyses adjusted for age, sex, and body mass index. The Expanded Disability Status Scale was associated with small HDL particle levels, inflammatory vulnerability, and metabolic vulnerability. Timed ambulation was associated with inflammatory vulnerability and metabolic vulnerability. Greater metabolic vulnerability and inflammatory vulnerability were associated with lower gray matter, deep gray matter volumes, and greater lateral ventricle volume. Serum-biomarker-derived indices of inflammatory and metabolic vulnerability are associated with disability and neurodegeneration in MS.

Alterations in Gray Matter Structural Networks in Amnestic Mild Cognitive Impairment: A Source-Based Morphometry Study.

Amnestic mild cognitive impairment (aMCI), considered as the prodromal stage of Alzheimer's disease, is characterized by isolated memory impairment and cerebral gray matter volume (GMV) alterations. Previous structural MRI studies in aMCI have been mainly based on univariate statistics using voxel-based morphometry. We investigated structural network differences between aMCI patients and cognitively normal older adults by using source-based morphometry, a multivariate approach that considers the relationship between voxels of various parts of the brain. Ninety-one aMCI patients and 80 cognitively normal controls underwent structural MRI and neuropsychological assessment. Spatially independent components (ICs) that covaried between participants were estimated and a multivariate analysis of covariance was performed with ICs as dependent variables, diagnosis as independent variable, and age, sex, education level, and site as covariates. aMCI patients exhibited reduced GMV in the precentral, temporo-cerebellar, frontal, and temporal network, and increased GMV in the left superior parietal network compared to controls (pFWER < 0.05, Holm-Bonferroni correction). Moreover, we found that diagnosis, more specifically aMCI, moderated the positive relationship between occipital network and Mini-Mental State Examination scores (pFWER < 0.05, Holm-Bonferroni correction). Our results showed GMV alterations in temporo-fronto-parieto-cerebellar networks in aMCI, extending previous results obtained with univariate approaches.