Brain communications最新文献

Frontotemporal dementia (FTD) is a prevalent form of early-onset dementia characterized by progressive neurodegeneration and encompasses a group of heterogeneous disorders. Due to overlapping symptoms, diagnosis of FTD and its subtypes still poses a challenge. Magnetic resonance imaging (MRI) is commonly used to support the diagnosis of FTD. Using machine learning and multivariate statistics, we tested whether brain atrophy patterns are associated with severity of cognitive impairment, whether this relationship differs between the phenotypic subtypes and whether we could use these brain patterns to classify patients according to their FTD variant. A total of 136 patients (70 behavioural variant FTD, 36 semantic variant primary progressive aphasia and 30 non-fluent variant primary progressive aphasia) from the frontotemporal lobar degeneration neuroimaging initiative (FTLDNI) database underwent brain MRI and clinical and neuropsychological examination. Deformation-based morphometry, which offers increased sensitivity to subtle local differences in structural image contrasts, was used to estimate regional cortical and subcortical atrophy. Atlas-based associations between atrophy values and performance across different cognitive tests were assessed using partial least squares. We then applied linear regression models to discern the group differences regarding the relationship between atrophy and cognitive decline in the three FTD phenotypes. Lastly, we assessed whether the combination of atrophy and cognition patterns in the latent variables identified in the partial least squares analysis could be used as features in a machine learning model to predict FTD subtypes in patients. Results revealed four significant latent variables that combined accounted for 86% of the shared covariance between cognitive and brain atrophy measures. Partial least squares-based atrophy and cognitive patterns predicted the FTD phenotypes with a cross-validated accuracy of 89.12%, with high specificity (91.46-97.15%) and sensitivity (84.19-93.56%). When using only MRI measures and two behavioural tests in the partial least squares and classification algorithms, ensuring clinical feasibility, our model was equally precise in the same participant sample (87.18%, specificity 76.14-92.00%, sensitivity 86.93-98.26%). Here, including only atrophy or behaviour patterns in the analysis led to prediction accuracies of 69.76% and 76.54%, respectively, highlighting the increased value of combining MRI and clinical measures in subtype classification. We demonstrate that the combination of brain atrophy and clinical characteristics and multivariate statistical methods can serve as a biomarker for disease phenotyping in FTD, whereby the inclusion of deformation-based morphometry measures adds to the classification accuracy in the absence of extensive clinical testing.

The deep brain stimulation (DBS) in the subthalamic nucleus (STN) has attracted more attention for primary Meige syndrome due to easier target location and lower power consumption. However, potential and reliable preoperative predictors of longitudinal outcomes of STN-DBS to guide therapeutic decisions remain largely unexplored. Herein, we used preoperative structural MRI and Burke-Fahn-Marsden Dystonia Rating Scale (BFMDRS) from 55 patients with primary Meige syndrome who finished STN-DBS after 5 years. They were further classified into response (n = 23) and super-response (n = 32) based on the improvement rates of BFMDRS. Voxel-based morphology, partial correlation analyses, receiver operating characteristic (ROC) analyses and support vector machine were performed. We identified that improved rates of BFMDRS were 63, 71.97, 76.64, 79.51, 81.02, 81.36, 81.16, 80.80 and 80.93% at 1, 3, 6, 12, 18, 24, 36, 48 and 60 months after STN-DBS, respectively, and remained steady across 1-5 years. Further voxel-based morphology analyses revealed significantly lower grey-matter volume in the right hippocampus, left putamen, right supramarginal gyrus and left superior frontal gyrus in response when compared with super-response. The grey-matter volumes in the left putamen, right supramarginal gyrus and left superior frontal gyrus were not only positively correlated with improvement rates of BFMDRS after STN-DBS for 5 years in the primary Meige syndrome, but also presented a reliable classification ability in distinguishing response and super-response (area under curve = 0.855). These results suggested that STN-DBS is an effective treatment for primary Meige syndrome, and preoperative grey-matter volume of putaminal-cortical circuits could be used as potential biomarkers to predict longitudinal outcomes.

This scientific commentary refers to 'Neural pathway activation in the subthalamic region depends on stimulation polarity' by Borgheai et al. (https://doi.org/10.1093/braincomms/fcaf006).

[This corrects the article DOI: 10.1093/braincomms/fcae432.].

Lesions in the CNS are frequently associated to a detrimental inflammatory reaction. In autoimmune neurodegenerative diseases, a proliferation-inducing ligand (APRIL) produced by CNS-infiltrating inflammatory cells binds to chondroitin sulphate proteoglycans (CSPGs). The latter are well-established obstacles to neural regeneration and remyelination in the CNS by interacting with receptor protein tyrosine phosphatase (RPTP) and Nogo receptor (NgR) families. Here, we are showing that APRIL blocks the interactions of RPTP and NgR with all types of chondroitin sulphate (CS). Functionally, APRIL neutralized the inhibitory effects of CS on mouse and human neuronal process growth. APRIL also blocked the inhibition of CS on mouse and human oligodendrocyte differentiation. Finally, APRIL increased myelination in an ex vivo organotypic model of demyelination in the presence of endogenous CSPG upregulation. Our data demonstrate the potential value for a recombinant form of soluble APRIL to achieve repair in the CNS.

This scientific commentary refers to 'Changes in neurotransmitter-related functional connectivity along the Alzheimer's disease continuum', by Manca et al. (https://doi.org/10.1093/braincomms/fcaf008).

Parkinson's disease genetic embraces genetic and non-genetic factors. It has been suggested a link between CAG repeat number in the HTT, ATXN1 and ATXN2 genes and different neurodegenerative diseases. Several genetic factors involved in Parkinson's disease development are indeed associated with cancer pathways. Moreover, several studies found a low prevalence of cancer in neurodegenerative diseases that can be associated with a low CAG repeat size in several genes. This study aimed to investigate the influence of CAG repeat sizes in ATXN1, ATXN2 and HTT genes on the risk for developing cancer and Parkinson's disease in a large cohort of patients with idiopathic Parkinson's disease and healthy controls. The work included 1052 patients with idiopathic Parkinson's disease and 1070 controls of European ancestry. CAG repeat sizes in HTT, ATXN1 and ATXN2 genes were analysed. Dunn's multiple comparison test for quantitative variables and logistic and linear regression were used. The long ATXN1 and HTT alleles and CAG size and both the ATXN2 short and long alleles were predictors for the Parkinson's disease risk. The long CAG ATXN1 allele gene was associated with the risk of cancer. No association was observed between CAG size in the HTT and ATXN2 genes and risk of cancer in patients with Parkinson's disease. We described an association of HTT, ATXN1 and ATXN2 with the risk of Parkinson's disease, which reinforce the hypothesis of the common pathway of neurodegeneration. Besides, ATXN1 could be a predictor of cancer risk among patients with Parkinson's disease, and these results suggest that cancer and neurodegeneration processes can share common pathways.

Polycystic ovary syndrome (PCOS) is characterized by excess androgens, ovulatory disorders and a higher prevalence of obesity and metabolic disturbances including Type 2 diabetes, hyperlipidaemia and hypertension, some of which are risk factors for neurodegenerative disorders such as Alzheimer's disease and brain atrophy. However, it is unclear whether brain ageing occurs more rapidly in women with PCOS compared with those without PCOS. Except for the hypothalamic-pituitary-gonadal axis involved in the conventional ovulatory process, little is known regarding the role of the grey matter in the pathogenesis of PCOS, and limited existing studies examining brain structures in PCOS have shown inconsistent results. This case-control study aimed to investigate the age-related differences in total and regional brain grey matter volume and average cortical thickness in young women with and without PCOS by using brain magnetic resonance imaging to understand whether women with PCOS exhibit distinctive patterns of brain ageing, and their association with factors including obesity, hyperandrogenism and metabolic disturbances. Seventy-six women diagnosed with PCOS and 68 age-matched women without PCOS (aged 20-35 years) underwent brain magnetic resonance imaging to measure grey matter volume and cortical thickness. Anthropometric, hormonal and metabolic measurements were conducted to assess their associations with the investigated brain structures. In women without PCOS, increasing age was significantly correlated with a decrease in global grey matter volume (r = -0.5598, P < 0.0001), while this association was not significant in women with PCOS (r = -0.1475, P = 0.204). The decline in grey matter volume with age differed significantly between the two groups regardless of obesity (body mass index exceeding 25 kg/m²), especially in the frontal, parietal, occipital and temporal regions. After adjusting for dehydroepiandrosterone sulphate (DHEAS) levels, the negative association between age and global grey matter volume became statistically significant in women with PCOS. Increasing age was also significantly associated with a decrease in global cortical thickness in women without PCOS, but not in women with PCOS. Such negative association between global cortical thickness and age was particularly stronger in women with obesity compared with those without. The negative association between age and global cortical thickness in women with PCOS became pronounced after adjusting for DHEAS levels. Women with PCOS experience a milder grey matter loss with age compared with women without PCOS. The neuroprotective effect of high DHEAS levels in women with PCOS may be implicated in this relationship.