Brain communications最新文献

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation method that has been increasingly used to treat psychiatric disorders, including tobacco use disorder. However, the neural mechanisms underlying the effects of rTMS remain unclear. This study aimed to examine the effectiveness of rTMS in smoking cessation and to explore the underlying neural mechanism of the treatment effect. In Experiment 1, we recruited 60 participants who smoked cigarettes and 60 healthy controls and used their baseline cerebral blood flow (CBF) measured by arterial spin labelling perfusion to determine the group-level difference in CBF. In Experiment 2, we used the left dorsolateral prefrontal cortex (DLPFC) as the target for subsequent 5-day rTMS treatment at a frequency of 10 Hz with 2000 pulses to observe the impact of rTMS on CBF, Fagerström test for nicotine dependence scores and Tiffney questionnaire on smoking urges scores. In Experiment 3, we measured functional connectivity to monitor the functional changes induced by rTMS and assessed their associations with smoking cravings and nicotine dependence scores. In Experiment 1, participants who smoked cigarettes presented significantly higher CBF in the left DLPFC and bilateral anterior cingulate cortex than healthy controls. In Experiment 2, rTMS significantly decreased CBF in the DLPFC and reduced Fagerström test for nicotine dependence scores and Tiffney questionnaire on smoking urges scores. In Experiment 3, rTMS increased functional connectivity between the left DLPFC and the bilateral superior frontal gyrus, right DLPFC, bilateral precuneus and bilateral parahippocampus in participants, who smoked cigarettes. Regional CBF is a tool to identify tobacco use disorder-related regional brain markers and targets for reducing nicotine dependence and smoking cravings through rTMS. A neural mechanism of left DLPFC rTMS may involve a reduction in CBF in the target area and an increase in functional connectivity between the target area and the DLPFC-striatal pathways.

Epilepsy is a cortico-subcortical network disease. Thalamo-cortical relationships in focal epilepsies, studied by stereoelectroencephalography in complex patients during pre-surgical evaluation, might help refine epilepsy surgery prognostic indicators and patient-specific treatments (i.e. thalamic deep brain stimulation). To this aim, we studied interictal thalamic traces, during rest and sleep recordings, in a cohort of 121 patients, delving into thalamo-cortical connectivity, hyperexcitability biomarkers and their correlation with treatment outcome. We retrospectively gathered stereoelectroencephalography recordings and clinical variables from patients who underwent stereoelectroencephalography with mainly a posterior-thalamic implantation, aiming at the pulvinar. Interictal recordings during rest and sleep were analysed to detect spikes and fast ripples automatically. Functional connectivity between the thalamus and other brain regions (involved or non-involved in the epileptogenic network) was examined using linear regression analysis. Higher thalamic hyperexcitability biomarker rates during sleep were linked to unfavourable surgical outcomes (Engel Class III/IV) compared to favourable outcomes (Engel Class I/II) (spikes: N = 117, P = 0.009, effect size = 0.25; fast ripples: N = 17, P = 0.036, effect size = 0.52). Thalamo-cortical functional connectivity analysis revealed heightened thalamic strength, particularly in the beta (P < 0.001, effect size = 0.38) and gamma (P = 0.012, effect size = 0.24) bands during sleep, among patients with poor surgical outcomes, especially with non-involved networks. Conversely, during rest, lower hyperexcitability biomarkers (spikes r = -0.2, P = 0.048; fast ripples r = -0.52, P = 0.045) and lower values of thalamic strength (delta band r = -0.28, P = 0.025; broadband r = -0.23, P = 0.01) were observed in patients with longer epilepsy duration. Furthermore, thalamic strength values during rest were lower in patients of older age (broadband r = -0.19, P = 0.045). These findings confirmed the important role of the thalamus in focal epilepsy. According to this exploratory group-level study, thalamic recordings could potentially improve pre-surgical assessment and help identify patients who may have a less severe outcome. Additionally, diminished thalamic activity and connectivity associated with epilepsy duration and age prompt speculation on the role of thalamo-cortical interactions in ageing-related physiological and pathological processes.

Alterations of resting state intrinsic functional networks have been associated with neurodegenerative diseases even before the onset of cognitive symptoms. Emerging hypotheses propose a role of resting state intrinsic functional networks alterations in the risk or vulnerability to neurodegeneration. It is unknown whether intrinsic functional network alterations can be causal for neurodegenerative diseases. We sought to answer this question using two-sample Mendelian randomization. Using the largest genome-wide association study of resting state intrinsic functional connectivity (n = 47 276), we generated genetic instruments (at the significance level 2.8 ×10^-11) to proxy resting state intrinsic functional network features. Based on the known brain regions implicated in different neurodegenerative diseases, we generated genetically proxied resting state intrinsic functional features and tested their association with their paired neurodegenerative outcomes: features in parieto-temporal regions and Alzheimer dementia (111 326 cases, 677 663 controls); frontal region and frontotemporal dementia (2154 cases, 4308 controls); temporal pole region and semantic dementia (308 cases, 616 controls), and occipital region with Lewy body dementia (LBD) (2591 cases, 4027 controls). Major depressive disorder outcome (170 756 cases, 329 443 controls) was included as a positive control and tested for its association with genetically proxied default mode network (DMN) exposure. Inverse-variance weighted analysis was used to estimate the association between the exposures (standard deviation units) and outcomes. Power and sensitivity analyses were completed to assess the robustness of the results. None of the genetically proxied functional network features were significantly associated with neurodegenerative outcomes (adjusted P value >0.05), despite sufficient calculated power. Two resting state features in the visual cortex showed a nominal level of association with LBD (P = 0.01), a finding that was replicated using a different instrument (P = 0.03). The genetically proxied DMN connectivity was associated with the risk of depression (P = 0.024), supporting the validity of the genetic instruments. Sensitivity analyses were supportive of the main results. This is the first study to comprehensively assess the potential causal effect of resting state intrinsic functional network features on the risk of neurodegeneration. Overall, the results do not support a causal role for the tested associations. However, we report a nominal association between visual network connectivity and Lewy body dementia that requires further evaluation.

Alzheimer's disease is associated with pre-symptomatic changes in brain morphometry and accumulation of abnormal tau and amyloid-beta pathology. Studying the development of brain changes prior to symptoms onset may lead to early diagnostic biomarkers and a better understanding of Alzheimer's disease pathophysiology. Alzheimer's disease pathology is thought to arise from a combination of protein accumulation and spreading via neural connections, but how these processes influence brain atrophy progression in the pre-symptomatic phases remains unclear. Individuals with a family history of Alzheimer's disease (FHAD) have an elevated risk of Alzheimer's disease, providing an opportunity to study the pre-symptomatic phase. Here, we used structural MRI from three databases (Alzheimer's Disease Neuroimaging Initiative, Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease and Montreal Adult Lifespan Study) to map atrophy progression in FHAD and Alzheimer's disease and assess the constraining effects of structural connectivity on atrophy progression. Cross-sectional and longitudinal data up to 4 years were used to perform atrophy progression analysis in FHAD and Alzheimer's disease compared with controls. PET radiotracers were also used to quantify the distribution of abnormal tau and amyloid-beta protein isoforms at baseline. We first derived cortical atrophy progression maps using deformation-based morphometry from 153 FHAD, 156 Alzheimer's disease and 116 controls with similar age, education and sex at baseline. We next examined the spatial relationship between atrophy progression and spatial patterns of tau aggregates and amyloid-beta plaques deposition, structural connectivity and neurotransmitter receptor and transporter distributions. Our results show that there were similar patterns of atrophy progression in FHAD and Alzheimer's disease, notably in the cingulate, temporal and parietal cortices, with more widespread and severe atrophy in Alzheimer's disease. Both tau and amyloid-beta pathology tended to accumulate in regions that were structurally connected in FHAD and Alzheimer's disease. The pattern of atrophy and its progression also aligned with existing structural connectivity in FHAD. In Alzheimer's disease, our findings suggest that atrophy progression results from pathology propagation that occurred earlier, on a previously intact connectome. Moreover, a relationship was found between serotonin receptor spatial distribution and atrophy progression in Alzheimer's disease. The current study demonstrates that regions showing atrophy progression in FHAD and Alzheimer's disease present with specific connectivity and cellular characteristics, uncovering some of the mechanisms involved in pre-clinical and clinical neurodegeneration.

Our Associate Editor, Laurent Sheybani, discusses some very old and very recent findings on sleep physiology and function, hoping to raise further interest and publications in the field.

Population-based proteomics offers a groundbreaking avenue to predict future disease risks, enhance our understanding of disease mechanisms, and discover novel therapeutic targets and biomarkers. The role of plasma proteins in dementia, however, requires further exploration. This study investigated 276 protein-dementia associations in 229 incident all-cause dementia, 89 Alzheimer's disease, and 41 vascular dementia among 3249 participants (55% women, 97.2% white ethnicity) from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up. We used Cox proportional hazard regression for the analysis. Receiver operating characteristic analyses were conducted to assess the precision of the identified proteins from the fully adjusted Cox regression models in predicting incident all-cause dementia, both individually and in combination with demographic predictors, APOE genotype, and memory score, to estimate the area under the curve. Additionally, the eXtreme Gradient Boosting machine learning algorithm was used to identify the most important features predictive of future all-cause dementia onset. These associations were then validated in 1506 incident all-cause dementia, 732 Alzheimer's disease, 281 vascular dementia, and 111 frontotemporal dementia cases among 52 745 individuals (53.9% women, 93.3% White ethnicity) from the UK Biobank over a median 13.7-year follow-up. Two-sample bi-directional Mendelian randomization and drug target Mendelian randomization were further employed to determine the causal direction between protein concentration and dementia. NEFL (hazard ratio [HR] [95% confidence intervals (CIs)]: 1.54 [1.29, 1.84]) and RPS6KB1 (HR [95% CI]: 1.33 [1.16, 1.52]) were robustly associated with incident all-cause dementia; MMP12 (HR [95% CI]: 2.06 [1.41, 2.99]) was associated with vascular dementia in ELSA, after correcting for multiple testing. Additional markers EDA2R and KIM1 were identified from subgroup and sensitivity analyses. Combining NEFL and RPS6KB1 with other predictors yielded high predictive accuracy (area under the curve = 0.871) for incident all-cause dementia. The eXtreme Gradient Boosting machine learning algorithm also identified RPS6KB1, NEFL, and KIM1 as the most important protein features for predicting future all-cause dementia. Sex difference was evident for the association between RPS6KB1 and all-cause dementia, with stronger association in men (P for interaction = 0.037). Replication in the UK Biobank confirmed the associations between the identified proteins and various dementia subtypes. The results from Mendelian randomization in the reverse direction indicated that several proteins serve as early markers for dementia, rather than being direct causes of the disease. These findings provide insights into putative mechanisms for dementia. Future studies are needed to validate the findings on RPS6KB1 in relation to dementia risk.

Moyamoya disease (MMD) may lead to perfusion deficits, stroke and brain atrophy in the long-term. Our aim was to analyse whole-brain volumetry of a large cohort of Moyamoya disease patients compared to healthy controls. 3D T1w MRI sequences of adult Moyamoya disease patients treated at our centre between 2016 and 2022 without prior revascularization were analysed for whole-brain volumetry (AssemblyNet) and compared age-controlled to healthy controls. A total of 133 different regions of interest were examined retrospectively for each patient separately by localization, structure and tissue type. All segmentations were subjected to automated and manual quality control. After quality control, 149 hemispheres from 80 Moyamoya disease patients were compared to 258 hemispheres from 129 healthy controls. A significant brain volume loss was observed in Moyamoya disease patients with increasing age, with the greatest reduction seen in bilaterally affected patients with Suzuki grade >3. As direct signs of brain atrophy, significant differences were seen across all regions of interests, emphasized in cortical grey matter with a reduction of 4.4% (95% CI 2.7-6.1%; P < 0.001) in patients aged 30-45 years and 3.4% (95% CI 2.1-4.7%; P < 0.001) aged 46-60 years. As indirect sign for atrophy, external CSF spaces increased up to 26.4% (95% CI 17.0-35.9%; P < 0.001) for 30-45 years and 28.4% (95% CI 17.1-39.7%; P < 0.001) for 46-60 years compared to healthy controls. Infratentorial, significant volume loss was observed for patients aged 46-60 years with 11.6% for cerebellar white matter (95% CI 3.7-19.5%; P = 0.0025) and with 8.5% (95% CI 3.5-13.5%; P = 0.0006) for the brainstem, likely due to secondary neurodegeneration. Moyamoya disease patients >45 year without ischaemia also had significantly less grey matter and white matter volume, with accordingly enlarged CSF spaces. Moyamoya disease may lead to significant differences in brain volume of local and global regions of interest as a sign of brain atrophy, even in the absence of infarctions. These findings might be useful for the understanding of the disease burden and in decision-making for timely revascularization.

Patient-tailored treatment, also known as precision-medicine, has been emphasized as a prioritized area in traumatic brain injury research. In fact, pre-injury patient genetic factors alone account for almost 26% of outcome prediction variance following traumatic brain injury. Among implicated genetic variants single-nucleotide polymorphism in apolipoprotein E has been linked to worse prognosis following traumatic brain injury, but the underlying mechanism is still unknown. We hypothesized that apolipoprotein E genotype would affect the levels of pathophysiology-driving structural, or inflammatory, proteins in cerebral microdialysate following severe traumatic brain injury. We conducted a prospective observational study of patients with severe traumatic brain injury treated with invasive neuromonitoring including cerebral microdialysis at Uppsala University Hospital. All patients were characterized regarding apolipoprotein E genotype. Utilizing fluid- and plate-based antibody arrays, we quantified 101 proteins (of which 89 were eligible for analysis) in cerebral microdialysate at 1 day and 3 days following trauma. Statistical analysis included clustering techniques, as well as uni- and multi-variate linear mixed modelling. In total, 26 patients were included, and all relevant genotypes of apolipoprotein E were represented in the data. Among all proteins tested, 41 proteins showed a time-dependent expression level. There was a weak clustering tendency in the data, and not primarily to genotype, either depicted through t-distributed stochastic neighbour embedding or hierarchical clustering. Using linear mixed models, two proteins [the inflammatory protein CD300 molecule like family member f (CLM-1) and the neurotrophic protein glial-derived neurotrophic factor family receptor α1] were found to have protein levels concomitantly dependent upon time and genotype, albeit this effect was not seen following multiple testing corrections. Apart from amyloid-β-40 (Aβ) and Microtubule-associated protein tau, neither Aβ peptide levels nor the Aβ42/40 ratio were seen related to time from trauma or apolipoprotein E genotype. This is the first study in clinical severe traumatic brain injury examining the influence of apolipoprotein E genotype on microdialysate protein expression. Protein levels in cerebral microdialysate following trauma are seen to be strongly dependent on time from trauma, corroborating previous work on protein expression longitudinally following traumatic brain injury. We also identified protein expression level alterations dependent on apolipoprotein E genotype, which might indicate that apolipoprotein E affects ongoing pathophysiology in the injured brain at the proteomic level.