Cerebellar White Matter Research Articles

Cerebellar Purkinje cell activity regulates white matter response and locomotor function after neonatal hypoxia.

Neonatal hypoxia (Hx) causes white matter (WM) injury, particularly in the cerebellum. We previously demonstrated Hx-induced reduction of cerebellar Purkinje cell (PC) activity results in locomotor deficits. Yet, the mechanism of Hx-induced cerebellar WM injury and associated locomotor abnormalities remains undetermined. Here, we show that the cerebellar WM injury and linked locomotor deficits are driven by PC activity and are reversed when PC activity is restored. Using optogenetics and multielectrode array recordings, we manipulated PC activity and captured the resulting cellular responses in WM oligodendrocyte precursor cells and GABAergic interneurons. To emulate the effects of Hx, we used light activated Halorhodopsin targeted specifically to the PC layer of normal mice. Suppression of PC firing activity at P13 and P21 phenocopied the locomotor deficits observed in Hx. Moreover, histopathologic analysis of the developing cerebellar WM following PC inhibition (P21) revealed a corresponding reduction in oligodendrocyte maturation and myelination, akin to our findings in Hx mice. Conversely, PC stimulation restored PC activity, promoted oligodendrocyte maturation and enhanced myelination, resulting in reversed Hx-induced locomotor deficits. Our findings highlight the crucial role of PC activity in cerebellar WM development and locomotor performance following neonatal injury.Significance statement Adult survivors of prematurity often experience locomotor incoordination secondary to cerebellar dysfunction. The cerebellum develops in the last trimester of pregnancy, a period that preterm neonates miss. Here, we show how neonatal hypoxia alters the crosstalk between neurons and oligodendrocytes in the developing cerebellum. Through loss-of-function and gain-of-function experiments, we unveiled that neuronal activity drives cerebellum-associated white matter injury and locomotor dysfunction after hypoxia. Importantly, restoring neuronal activity using direct neurophysiological stimulation reversed the hypoxia-induced white matter injury and locomotor deficits. Early cerebellar neuronal stimulation could serve as a potential therapeutic intervention for locomotor dysfunction in neonates.

Beyond IQ: executive function deficits and their relation to functional, clinical, and neuroimaging outcomes in 3q29 deletion syndrome.

3q29 deletion syndrome (3q29del) is a rare (~1:30 000) genomic disorder associated with a wide array of neurodevelopmental and psychiatric phenotypes. Prior work by our team identified clinically significant executive function (EF) deficits in 47% of individuals with 3q29del; however, the nuances of EF in this population have not been described. We used the Behavior Rating Inventory of Executive Function (BRIEF) to perform the first in-depth assessment of real-world EF in a cohort of 32 individuals with 3q29del (62.5% male, mean age = 14.5 ± 8.3 years). All participants were also evaluated with gold-standard neuropsychiatric and cognitive assessments. High-resolution structural magnetic resonance imaging was performed on a subset of participants (n = 24). We found global deficits in EF; individuals with 3q29del scored higher than the population mean on the BRIEF global executive composite (GEC) and all subscales. In total, 81.3% of study subjects (n = 26) scored in the clinical range on at least one BRIEF subscale. BRIEF GEC T scores were higher among 3q29del participants with a diagnosis of attention deficit/hyperactivity disorder (ADHD), and BRIEF GEC T scores were associated with schizophrenia spectrum symptoms as measured by the Structured Interview for Psychosis-Risk Syndromes. BRIEF GEC T scores were not associated with cognitive ability. The BRIEF-2 ADHD form accurately (sensitivity = 86.7%) classified individuals with 3q29del based on ADHD diagnosis status. BRIEF GEC T scores were correlated with cerebellar white matter and subregional cerebellar cortex volumes. Together, these data expand our understanding of the phenotypic spectrum of 3q29del and identify EF as a core feature linked to both psychiatric and neuroanatomical features of the syndrome.

The characteristics of three-dimensional real inversion recovery MRI sequence in delayed Meniere’s disease

Background Delayed Meniere’s disease (DMD) is characterized by episodic vertigo occurring after a significant delay following longstanding sensorineural hearing loss. However, its pathogenesis and characteristics remain unclear. Objectives To explore the characteristics of endolymphatic hydrops (EH) in DMD and investigate its pathology using MRI. Materials and methods 17 patients diagnosed with DMD were included in this study. A 3D-real-IR MRI sequence was utilized to visualize and grade EH. The signal intensity of the basal cochlear turn was compared to that of the cerebellar white matter to calculate the CC ratio. A paired t-test was employed to assess the differences in the CC ratio between the affected and unaffected sides in patients with unilateral DMD. Results EH was present in 100% of cases on the affected side. Unilateral EH was seen in 82.4%, and bilateral EH in 23.5%. Vestibular EH without cochlear involvement occurred in 29.4%, and cochlear EH without vestibular involvement in 5.9%. The CC ratio was significantly higher on the affected side (p < 0.01). Conclusions and significance A significant grade of vestibular EH was observed in DMD, with bilateral EH being relatively common. The increased contrast on the affected side may reflect the underlying pathology in DMD patients.

Arbor vitae cerebelli: Fractal properties and their quantitative assessment by novel “contour scaling” fractal analysis method (an anatomical study)

BackgroundArbor vitae cerebelli (tree-like branching white matter of the cerebellum) has a complex spatial configuration that is challenging to assess using conventional morphometric methods. This study proposes a fractal approach to describe and quantify the anatomy of Arbor vitae cerebelli. For this purpose, a new “contour scaling” method for fractal analysis of cerebellar white matter was developed. Material and methodsThe cerebella of 100 cadavers (50 male and 50 female) who died from causes unrelated to brain pathology, aged 20–95 years, were examined. Mid-sagittal sections of the cerebellar vermis were studied. The fractal dimension values of the cerebellar white matter were determined using both the developed fractal analysis method and the conventional “box counting” method, along with measurements of non-fractal parameters including cerebellar weight, area and perimeter of the vermis cross-section, perimeter-to-area ratio, and circularity. ResultsConsidering the cerebellar white matter as a tree-like fractal, it was found to have 7 or 8 primary branches, which subdivide into 10-18 second-iteration branches, 19–38 third-iteration branches, and 34–53 fourth-iteration branches. Females more often had 8 primary branches compared to males, while males had a greater number of branches in the second to fourth iterations. The mean fractal (Hausdorff) dimension was 1.697 (1.721 in males, 1.674 in females, P = 0.01). The fractal dimension correlated most strongly with the perimeter and area of the vermis cross-section and had no significant relationship with age. ConclusionThe fractal (Hausdorff) dimension, determined using the novel “contour scaling” method, quantitatively assesses the degree of branching of the cerebellar white matter. An increase in the absolute size of the cerebellum leads to a higher degree of branching of its white matter and an increase in the number of its constitutive components – white matter branches and folia.

Quantitative assessment of gadolinium deposition in dentate nuclei with MR fingerprinting

Gadolinium deposition in the dentate nucleus (DN) has been evaluated by T1-weighted imaging (T1WI) and T1 (R1) mapping, but not MR fingerprinting (MRF). This study investigated associations between T1 and T2 values of DN and gadolinium-based contrast agents (GBCAs) using 2-dimensional MRF. This study included 101 patients. Region of interest analysis was performed for T1 and T2 values of DN on MRF (T1-MRF, T2-MRF) and T1-weighted images (T1WI ratio). T1 and T2 ratios compared to normal cerebellar white matter (T1-MRF ratio, T2-MRF ratio) were calculated. The type of previous GBCA was confirmed in 79 patients, and linear regressions were performed between T1, T2 values and number of GBCAs. Good correlations were observed between T1-MRF and T1WI ratio (ρ=-0.69, P<0.001) and between T1-MRF ratio and T1WI ratio (ρ=-0.76, P<0.001). Mild correlations were observed between T2-MRF and T1WI ratio (ρ=-0.32, P<0.001) and between T2-MRF ratio and T1WI ratio (ρ=-0.44, P<0.001). The number of linear-type GBCAs was associated with T1-MRF (β=-0.62, P<0.001) and T1-MRF ratio (β=-0.54, P<0.001) in univariate linear regression analyses, and with T1-MRF (β=-0.61, P<0.001) and T1-MRF ratio (β=-0.53, P<0.001) in multivariate analysis. The number of linear-type GBCAs was associated with T2-MRF (β=-0.30, P<0.001) and T2-MRF ratio (β=-0.29, P<0.001) in univariate analyses, and with T2-MRF (β=-0.31, P<0.001) and T2-MRF ratio (β=-0.32, P<0.001) in multivariate analyses. No associations were observed between number of macrocyclic GBCAs and T1-MRF (ratio) or T2-MRF (ratio). The number of linear-type GBCA administrations was associated with lower T1 and T2 values (ratios) in DN.

Development and Validation of Artificial Intelligence Models for Prognosis Prediction of Juvenile Myoclonic Epilepsy with Clinical and Radiological Features.

Background: Juvenile myoclonic epilepsy (JME) is a common adolescent epilepsy characterized by myoclonic, generalized tonic-clonic, and sometimes absence seizures. Prognosis varies, with many patients experiencing relapse despite pharmacological treatment. Recent advances in imaging and artificial intelligence suggest that combining microstructural brain changes with traditional clinical variables can enhance potential prognostic biomarkers identification. Methods: A retrospective study was conducted on patients with JME at the Severance Hospital, analyzing clinical variables and magnetic resonance imaging (MRI) data. Machine learning models were developed to predict prognosis using clinical and radiological features. Results: The study utilized six machine learning models, with the XGBoost model demonstrating the highest predictive accuracy (AUROC 0.700). Combining clinical and MRI data outperformed models using either type of data alone. The key features identified through a Shapley additive explanation analysis included the volumes of the left cerebellum white matter, right thalamus, and left globus pallidus. Conclusions: This study demonstrated that integrating clinical and radiological data enhances the predictive accuracy of JME prognosis. Combining these neuroanatomical features with clinical variables provided a robust prediction of JME prognosis, highlighting the importance of integrating multimodal data for accurate prognosis.

Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning.

Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.

Effect of Ethanol on Brain Electrical Tissue Conductivity in Social Drinkers.

How the biophysics of electrical conductivity measures relate to brain activity is poorly understood. The sedative, ethanol, reduces metabolic activity but its impact on brain electrical conductivity is unknown. To investigate whether ethanol reduces brain electrical tissue conductivity. Prospective. Fifty-two healthy volunteers (aged 18-37 years, 22 females, 30 males). 3 T, T1-weighted, multi-shot, turbo-field echo (TFE); 3D balanced fast-field echo (bFFE). Brain gray and white matter tissue conductivity measured with phase-based magnetic resonance electrical properties tomography (MREPT) compared before and 20 minutes after ethanol consumption (0.7 g/kg body weight). Differential conductivity whole brain maps were generated for three subgroups: those with strong ( > 0.1 S/m; N = 33), weak (0.02 S/m ≤ ≤ 0.1 S/m; N = 9) conductivity decrease, and no significant response ( < 0.02 S/m, N = 10). Maps were compared in the strong response group where breath alcohol rose between scans, vs. those where it fell. Average breath alcohol levels were compared to the differential conductivity maps using linear regression. T-maps were generated (threshold P < 0.05 and P < 0.001; minimum cluster 48 mm3). Differential conductivity maps were compared with ANOVA. Whole-group analysis showed decreased conductivity that did not survive statistical thresholding. Strong responders (N = 33) showed a consistent pattern of significantly decreased conductivity ( > 0.1 S/m) in frontal/occipital and cerebellar white matter. The weak response group (N = 9) showed a similar pattern of conductivity decrease (0.02 S/m ≤ ≤ 0.1 S/m). There was no significant relationship with breath alcohol levels, alcohol use, age, ethnicity, or sex. The strong responders' regional response was different between ascending (N = 12) or descending (N = 20) alcohol during the scan. Ethanol reduces brain tissue conductivity in a participant-dependent and spatially dependent fashion. 1 TECHNICAL EFFICACY: Stage 2.

Multimodal, Longitudinal Profiling of SCA1 Identifies Predictors of Disease Severity and Progression.

Spinocerebellar ataxia type 1 (SCA1) is a rare autosomal dominant neurodegenerative disease. Objective surrogate markers sensitive to detect changes in disease severity are needed to reduce sample sizes in interventional trials and identification of predictors of faster disease progression would facilitate patient selection, enrichment, or stratification in such trials. We performed a prospective 1-year longitudinal, multimodal study in 34 ataxic SCA1 individuals and 21 healthy controls. We collected clinical, patient-reported outcomes, biochemical and magnetic resonance (MR) biomarkers at baseline and after 1 year. We determined 1-year progression and evaluated the potential predictive value of several baseline markers on 1-year disease progression. At baseline, multiple structural and spectroscopic MR markers in pons and cerebellum differentiated SCA1 from healthy controls and correlated with disease severity. Plasma and cerebrospinal fluid (CSF) neurofilament light (NfL) chain and CSF glial fibrillary acidic protein (GFAP) were elevated in SCA1. In longitudinal analysis, total brainstem and pontine volume change, inventory of non-ataxia signs (INAS) count, and SCA functional index (SCAFI) showed larger responsiveness compared to the Scale for Assessment and Rating of Ataxia (SARA). Longer disease duration, longer non-expanded CAG repeat length, and higher disease burden were associated with faster SARA increase after 1-year in the SCA1 group. Similarly, lower baseline brainstem, pontine, and cerebellar volumes, as well as lower levels of N-acetylaspartate and glutamate in the cerebellar white matter, were also associated with faster SARA increase. Our results guide the selection of the most sensitive measures of disease progression in SCA1 and have identified features associated with accelerated progression that could inform the design of clinical trials. ANN NEUROL 2024.

A preclinical mice model of multiple sclerosis based on the toxin-induced double-site demyelination of callosal and cerebellar fibers

BackgroundMultiple sclerosis (MS) is an irreversible progressive CNS pathology characterized by the loss of myelin (i.e. demyelination). The lack of myelin is followed by a progressive neurodegeneration triggering symptoms as diverse as fatigue, motor, locomotor and sensory impairments and/or bladder, cardiac and respiratory dysfunction. Even though there are more than fourteen approved treatments for reducing MS progression, there are still no cure for the disease. Thus, MS research is a very active field and therefore we count with different experimental animal models for studying mechanisms of demyelination and myelin repair, however, we still lack a preclinical MS model assembling demyelination mechanisms with relevant clinical-like signs.ResultsHere, by inducing the simultaneous demyelination of both callosal and cerebellar white matter fibers by the double-site injection of lysolecithin (LPC), we were able to reproduce CNS demyelination, astrocyte recruitment and increases levels of proinflammatory cytokines levels along with motor, locomotor and urinary impairment, as well as cardiac and respiratory dysfunction, in the same animal model. Single site LPC-injections either in corpus callosum or cerebellum only, fails in to reproduce such a complete range of MS-like signs.ConclusionWe here report that the double-site LPC injections treatment evoke a complex MS-like mice model. We hope that this experimental approach will help to deepen our knowledge about the mechanisms of demyelinated diseases such as MS.

Exploring the genetic architecture of brain structure and ADHD using polygenic neuroimaging-derived scores.

Genome-wide association studies (GWAS) have provided valuable insights into the genetic basis of neuropsychiatric disorders and highlighted their complexity. Careful consideration of the polygenicity and complex genetic architecture could aid in the understanding of the underlying brain mechanisms. We introduce an innovative approach to polygenic scoring, utilizing imaging-derived phenotypes (IDPs) to predict a clinical phenotype. We leveraged IDP GWAS data from the UK Biobank, to create polygenic imaging-derived scores (PIDSs). As a proof-of-concept, we assessed genetic variations in brain structure between individuals with ADHD and unaffected controls across three NeuroIMAGE waves (n = 954). Out of the 94 PIDS, 72 exhibited significant associations with their corresponding IDPs in an independent sample. Notably, several global measures, including cerebellum white matter, cerebellum cortex, and cerebral white matter, displayed substantial variance explained for their respective IDPs, ranging from 3% to 5.7%. Conversely, the associations between each IDP and the clinical ADHD phenotype were relatively weak. These findings highlight the growing power of GWAS in structural neuroimaging traits, enabling the construction of polygenic scores that accurately reflect the underlying polygenic architecture. However, to establish robust connections between PIDS and behavioral or clinical traits such as ADHD, larger samples are needed. Our novel approach to polygenic risk scoring offers a valuable tool for researchers in the field of psychiatric genetics.

Enzyme Replacement Therapy for CLN2 Disease: MRI Volumetry Shows Significantly Slower Volume Loss Compared with a Natural History Cohort.

Neuronal ceroid lipofuscinoses are a group of neurodegenerative disorders. Recently, enzyme replacement therapy (ERT) was approved for neuronal ceroid lipofuscinosis type 2 (CLN2), a subtype of neuronal ceroid lipofuscinoses. The aim of this study was to quantify brain volume loss in CLN2 disease in patients on ERT in comparison with a natural history cohort using MRI. Nineteen (14 female, 5 male) patients with CLN2 disease at 1 UK center were studied using serial 3D T1-weighted MRI (follow-up time, 1-9 years). Brain segmentation was performed using FreeSurfer. Volume measurements for supratentorial gray and white matter, deep gray matter (basal ganglia/thalami), the lateral ventricles, and cerebellar gray and white matter were recorded. The volume change with time was analyzed using a linear mixed-effects model excluding scans before treatment onset. Comparison was made with a published natural history cohort of 12 patients (8 female, 4 male), which was re-analyzed using the same method. Brain volume loss of all segmented brain regions was much slower in treated patients compared with the natural history cohort. For example, supratentorial gray matter volume in treated patients decreased by a mean of 3% (SD, 0.74%) (P < .001) annually compared with an annual volume loss of a mean of 16.8% (SD, 1.5%) (P < .001) in the natural history cohort. Our treatment cohort showed a significantly slower rate of brain parenchymal volume loss compared with a natural history cohort in several anatomic regions. Our results complement prior clinical data that found a positive response to ERT. We demonstrate that automated MRI volumetry is a sensitive tool to monitor treatment response in children with CLN2 disease.

White Matter Abnormalities Track Disease Progression in Multiple System Atrophy.

White matter (WM) abnormalities have been implicated in clinically relevant functional decline in multiple system atrophy (MSA). To identify the WM and gray matter (GM) abnormalities in MSA and assess the utility of longitudinal structural and diffusion changes as surrogate markers for tracking disease progression in MSA. Twenty-seven participants with early MSA [15 with clinically predominant cerebellar (MSA-C) and 12 with clinically predominant parkinsonian features (MSA-P)] and 14 controls were enrolled as a part of our prospective, longitudinal study of synucleinopathies. Using structural magnetic resonance imaging (MRI) and diffusion MRI (diffusion tensor and neurite orientation and dispersion density imaging), we analyzed whole and regional brain changes in these participants. We also evaluated temporal imaging trajectories based on up to three annual follow-up scans and assessed the impact of baseline diagnosis on these imaging biomarkers using mixed-effect models. MSA patients exhibited more widespread WM changes than GM, particularly in the cerebellum and brainstem, with greater severity in MSA-C. Structural and diffusion measures in the cerebellum WM and brainstem deteriorated with disease progression. Rates of progression of these abnormalities were similar in both MSA subtypes, reflecting increasing overlap of clinical features over time. WM abnormalities are core features of MSA disease progression and advance at similar rates in clinical MSA subtypes. Multimodal MRI imaging reveals novel insights into the distribution and pattern of brain abnormalities and their progression in MSA. Selected structural and diffusion measures may be useful for tracking disease progression in MSA clinical trials.

Microstructural Properties of the Cerebellar Peduncles in Children With Developmental Language Disorder.

Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of corticostriatal systems, little is known about the role of the cerebellum in DLD. Corticocerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of children with DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not corticocerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech and language acquisition and development.

Understanding the role of cerebellum in early Parkinson’s disease: a structural and functional MRI study

Increasing evidence suggests that the cerebellum may have a role in the pathophysiology of Parkinson’s disease (PD). Hence, the scope of this study was to investigate whether there are structural and functional alterations of the cerebellum and whether they correlate with motor and non-motor symptoms in early PD patients. Seventy-six patients with early PD and thirty-one age and sex-matched healthy subjects (HS) were enrolled and underwent a 3 T magnetic resonance imaging (MRI) protocol. The following MRI analyses were performed: (1) volumes of 5 cerebellar regions of interest (sensorimotor and cognitive cerebellum, dentate, interposed, and fastigial nuclei); (2) microstructural integrity of the cerebellar white matter connections (inferior, middle, and superior cerebellar peduncles); (3) functional connectivity at rest of the 5 regions of interest already described in point 1 with the rest of brain. Compared to controls, early PD patients showed a significant decrease in gray matter volume of the dentate, interposed and fastigial nuclei, bilaterally. They also showed abnormal, bilateral white matter microstructural integrity in all 3 cerebellar peduncles. Functional connectivity of the 5 cerebellar regions of interest with several areas in the midbrain, basal ganglia and cerebral cortex was altered. Finally, there was a positive correlation between abnormal functional connectivity of the fastigial nucleus with the volume of the nucleus itself and a negative correlation with axial symptoms severity. Our results showed that structural and functional alterations of the cerebellum are present in PD patients and these changes contribute to the pathophysiology of PD in the early phase.

Computer-Based Evaluation of α-Synuclein Pathology in Multiple System Atrophy as a Novel Tool to Recognize Disease Subtypes

Multiple system atrophy (MSA) is a neurodegenerative disorder with variable disease course and distinct constellations of clinical (cerebellar [MSA-C] or parkinsonism [MSA-P]) and pathological phenotypes, suggestive of distinct α-synuclein (αSyn) strains. Neuropathologically, MSA is characterized by the accumulation of αSyn in oligodendrocytic glial cytoplasmic inclusions (GCI). Using a novel computer-based method, this study quantified the size of GCIs, density of all αSyn pathology, density of only the GCIs, and number of GCIs in MSA cases (n = 20). The putamen and cerebellar white matter were immunostained with the disease-associated 5G4 anti-αSyn antibody. Following digital scanning and image processing, total 5G4-immunoreactive pathology (ie, neuronal, neuritic, and glial) and GCIs were optically dissected for inclusion size and density measurement and then evaluated applying a novel computer-based method using ImageJ. GCI size varied between cases and brain regions (P < .0001), and heterogeneity in the density of all αSyn pathology including the density and number of GCIs were observed between regions and across cases, where MSA-C cases had a significantly higher density of all αSyn pathology in the cerebellar white matter (P = .049). Some region-specific morphologic variables inversely correlated with the age of onset and death, suggestive of an underlying aging-related cellular mechanism. Unsupervised K-means cluster analysis classified MSA cases into 3 distinct groups based on region-specific morphologic variables. In conclusion, we developed a novel computer-based method that is easily accessible, providing a first step to developing artificial intelligence–based evaluation strategies for large scale comparative studies. Our observations on the variability of morphologic variables between brain regions and cases highlight (1) the importance of computer-based approaches to detect features not considered in the routine diagnostic practice, and (2) novel aspects for the identification of previously unrecognized MSA subtypes that do not necessarily reflect the current clinical classification of MSA-C or MSA-P.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning.

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye. This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature. The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Comparison of detailed brain volume measurements in schizophrenia with healthy individuals

Aims: This study aims to provide insights into the preliminary and definitive diagnosis, treatment modalities, and elucidation of the underlying pathophysiological mechanisms of schizophrenia, alongside ongoing research efforts. Methods: This retrospective study examined a cohort of 31 patients (17 male, 14 female) diagnosed with schizophrenia according to the criteria specified in the Diagnostic and Statistical Manual of Mental Disorders (DSM V) at the Psychiatry Clinic of the University Hospital. The healthy control group, comprising 31 individuals (17 male, 14 female), was selected from archived records at the same hospital, with sociodemographic characteristics matching those of the patient group. High-resolution T1-weighted MRI images of the individuals were analyzed using the VolBrain AssemblyNet program. Results: It has been observed that the volumes of various brain structures, including white matter (WM), gray matter (GM), subcortical GM, cortical GM, WM+GM, IC, total cerebrum, right cerebrum, left cerebrum, cerebrum WM, cerebrum GM, are significantly reduced (p&lt;0.05) in patients diagnosed with schizophrenia compared to healthy controls. Additionally, volumes of cerebellar WM, GM, vermis, brainstem, accumbens, hippocampus, thalamus, ventral diencephalon (DC), amygdala, and basal forebrain were found to be decreased in schizophrenia patients compared to healthy controls. Conversely, volumes of the pallidum, caudate, putamen, inferior lateral ventricle, lateral ventricle, third ventricle, fourth ventricle, and CSF were observed to be increased. Conclusion: These findings underscore the widespread nature of neuroanatomical alterations in schizophrenia and highlight the importance of understanding these changes for elucidating the disorder's pathophysiology and developing targeted therapeutic interventions. Further research is warranted to explore the interplay of genetic, developmental, and environmental factors in schizophrenia.

Cerebellar gray matter and white matter damage among older adults with prediabetes

AimsTo investigate alterations in cerebrum and cerebellum in prediabetes. Cerebellar injury in diabetes is traceable, but it has not been systematically studied, and whether cerebellar injury occurs and the degree of damage in prediabetes are not known. MethodsThe current study investigated cerebral and cerebellar gray matter volume, white matter volume, white matter microstructure and white matter hyperintensity on T1-weighted, T2-weighted fluid-attenuated inversion recovery and diffusion tensor imaging scans in 78 individuals with normal glucose metabolism, 92 with prediabetes, and 108 with type 2 diabetes. ResultsParticipants with prediabetes showed significant gray matter and white matter atrophy, microstructural damage in the cerebellar and cerebral regions. Additionally, widespread structural alterations were observed in the diabetic stage. The function of the damaged brain area was further decoded in Neurosynth, and the damaged cerebellar area with prediabetic lesions was closely related to motor function, while the area affected by diabetes was related to complex cognitive function in addition to motor function. ConclusionsCerebellar injury had already appeared in the prediabetic stage, and cerebellar injury was aggravated in the diabetic stage; therefore, the cerebellum is a key area that is damaged early in the development of diabetes.

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles

Abstract Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine–adenine–guanine repeat number. Moreover, the transcriptome–connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.