Brain communications最新文献

Mild traumatic brain injury is a risk factor to sustaining future mild traumatic brain injuries and increased symptom severity and duration following a second mild traumatic brain injury. Inflammation during neurobiological recovery is hypothesized to influence susceptibility to poorer outcomes after repetitive mild traumatic brain injury. Here, we investigated whether the inflammatory response during neurobiological recovery is related to susceptibility to increased functional and biological deficits following re-injury. To investigate this, we collected serum 1, 3, 7 or 14 days after mild traumatic brain injury in male Sprague-Dawley rats and measured levels of circulating inflammatory cytokines using the MesoScale Discovery MESO QuickPlex SQ 120MM platform to quantify interferon-gamma, interleukin-1-beta, interleukin-4, interleukin-5, interleukin-6, interleukin-10, interleukin-13, keratinocyte chemoattractant/human growth-related oncogene and tumour necrosis factor-alpha. Immediately following this blood collection, rats were given a second mild traumatic brain injury to assess associations between cytokine levels at time of second mild traumatic brain injury with behavioural outcomes, neurofilament light levels, and ex vivo diffusion tensor imaging in the 28 days following second injury. After a single mild traumatic brain injury, interleukin-10, interleukin-13, interleukin-4 and tumour necrosis factor-alpha were elevated 3 days post-injury while interleukin-10 and tumour necrosis factor-alpha levels were elevated 14-days post-injury. Furthermore, higher levels of interleukin-6 and interleukin-13 at the time of a second mild traumatic brain injury were associated with a reduced number of acute neurological signs of mild traumatic brain injury following the second injury. There were no other significant correlations between circulating cytokine levels and post-injury outcomes following correction for multiple comparisons. These findings provide initial, hypothesis-generating evidence that higher levels of circulating inflammatory cytokines at the time of a second mild traumatic brain injury may be associated with decreased susceptibility to a second mild traumatic brain injury, highlighting the complex role of inflammation in repeated mild traumatic brain injury.

Frontotemporal lobar degeneration is associated with diverse clinical phenotypes underlain by multiple disease pathologies and genetic mutations for which traditional structural magnetic resonance imaging (MRI) analyses lack discriminatory sensitivity and specificity. Here, we use a data-driven multivariate method to extract a concise set of MRI-derived shape morphometric features and cross-sectionally examine the discriminatory capability of their unique combinations in three frontotemporal lobar degeneration clinical phenotypes. Patients with sporadic or familial frontotemporal lobar degeneration clinical syndromes across two cohorts (i.e. behavioral variant (n = 173), non-fluent variant primary progressive aphasia (n = 63), semantic variant primary progressive aphasia (n = 41)) and 158 controls were assessed. Cortical morphometry measures of cortical thickness, surface curvature, and metric distortion were extracted, contrasted with controls using linear models, and additionally entered into a sparse partial least squares discriminatory analysis (sPLS-DA) designed to model multimodal signatures unique to each phenotype. Discriminatory power of partial least squares-derived features was tested on independent, age-matched test data. We found that each cortical morphometric feature significantly differed between clinical syndromes in dissociable spatial patterns. On independent data, the combination of cortical thickness and surface curvature best discriminated between behavioural variant and non-fluent variant primary progressive aphasia patients from controls. For semantic variant primary progressive aphasia, any model including cortical thickness maximized model performance. The sparse partial least squares approach indicated distinctive brain regions contribute to discrimination for each shape feature, suggesting each feature may reflect unique aspects of neurodegeneration across groups. This method could prove invaluable in future studies for early detection of frontotemporal lobar degeneration phenotypes.

Numerous correlational and group comparison studies have demonstrated robust associations between sleep health (SH) and large-scale brain organization. However, individual differences play a critical role in this relationship, highlighting the need for person-specific analyses. In this study, we aimed to explore whether multiple brain imaging features could predict various SH-related traits at the individual level using machine learning (ML) techniques. We utilized data from 28 088 participants in the UK Biobank, extracting 4677 structural and functional neuroimaging markers. These features were then used to predict a range of self-reported sleep characteristics, including insomnia symptoms, sleep duration, ease of waking in the morning, chronotype, napping behaviour, daytime sleepiness and snoring. For each of these seven traits, we trained both linear and nonlinear ML models to evaluate how well brain imaging data could account for individual differences. Our analyses involved extensive computational resources, equivalent to over 200 000 core-hours (equivalent to 25 years of compute time). Despite this, the predictive performance of brain features was consistently low across all models, with balanced accuracy scores ranging from 0.50 to 0.59. The highest accuracy achieved (0.59) came from a linear model predicting the ease of getting up in the morning. Notably, models using only demographic variables such as age and sex achieved comparable performance, suggesting that these basic characteristics may largely explain the observed variability. These findings indicate that, even when using a large, well-powered sample and advanced ML techniques, multi-modal brain imaging features provide limited predictive value for SH at the individual level. This low predictability underscores the complexity of the relationship between self-reported sleep and brain structure/function. It also suggests that other biological, environmental or psychological factors-possibly not captured by current imaging modalities-may play a more substantial role in shaping sleep-related behaviours.

Optic nerve is now included as a fifth typical location in multiple sclerosis diagnosis criteria. The radiologically isolated syndrome represents the earliest stage of multiple sclerosis. Previous optical coherence tomography studies in this asymptomatic context reported no or slight retinal thickness difference compared to healthy subjects. Frequency of asymptomatic optic nerve lesions has never been evaluated at this stage of the disease. Susceptibility-weighted imaging findings on brain MRI are incorporated in the recent revised multiple sclerosis diagnostic criteria (2024) through the 'central vein sign' and 'paramagnetic rim lesion' parameters but for the diagnosis of asymptomatic form, 'paramagnetic rim lesion' are not included. In this study, we aim to measure the frequency of optic nerve lesions in radiologically isolated syndrome and to evaluate their impact on retinal thicknesses. Second, we aim to evaluate the association of optic nerve lesion and susceptibility-weighted imaging parameters with the disease course. This retrospective cohort study collected data (August 2020 to December 2024) on patients with radiologically isolated syndrome at Lille (France). MRI was performed at baseline and every year. Optic nerves were studied using MRI and optical coherence tomography performed on the same day by measuring retinal thickness intereye difference. Clinical examination was performed every 6 months. We included 32 untreated patients (63 eyes; one eye excluded due to the fortuitous discovery of an ocular melanoma). Nine optic nerves showed lesions on MRI in the orbital or canalicular part. These eyes had a thinner peripapillary retinal nerve fibre layer compared to eyes without optic nerve lesions on MRI (median = 87.4 µm versus 96.8 µm, P = 0.003). No association was found between peripapillary retinal nerve fibre layer thickness and quantitative MRI parameters as optic radiations T2 lesions or primary visual cortex volumes. During follow-up (median: 22.1 months), three patients converted to relapsing multiple sclerosis and two patients to progressive multiple sclerosis. Among them, 60% had an optic nerve lesion (versus 25.9%) and 60% had at least one paramagnetic rim lesion (versus 25.9%). In total, 24 patients fulfilled dissemination in time and space according to the revised multiple sclerosis diagnostic criteria (2024). As in clinically isolated syndrome and clinically definite MS, silent optic nerve lesions seem to be the main cause of subclinical retinal neuro-axonal loss at radiologically isolated syndrome stage. Our results suggest that patients with paramagnetic rim lesion or optic nerve lesion might present a higher risk of clinical conversion.

Although network neuropsychology is a promising approach to the study of clinical profiles, the link between Alzheimer's disease (AD) biomarkers and neuropsychological networks is still undetermined. We hypothesized that network differences would exist between biomarker-positive and biomarker-negative participants, and that these would be driven by network nodes corresponding to performance on tests of episodic memory, as this is the cognitive domain most distinctively affected by AD since the earliest clinical stages. In this case-control study, we investigated sub-cohorts of individuals who had been (i) enrolled in the National Alzheimer's Coordinating Center initiative and (ii) tested with Version 3 of the Uniform Data Set neuropsychological battery (i.e. consisting of 11 tests). These included 1263 'β-amyloid positive' (A+), 1594 'β-amyloid negative' (A-), 442 'β-amyloid and hyperphosphorylated tau positive' (A + T+) and 734 'β-amyloid and hyperphosphorylated tau negative' (A-T-) participants. We first calculated neuropsychological residuals by regressing out age, years of education, sex, Clinical Dementia Rating scores and timepoint distance between neuropsychological and biomarker assessment. Secondly, we used rank-based correlations to define conditional associations across all pairs of test scores (i.e. the nodes of the network). Thirdly, we imposed a penalty (i.e. via the Least Absolute Shrinkage and Selection Operator method) to control for network sparsity. We then tested for differences in global network metrics and node centrality between A+ and A- and between A+T+ and A-T- participants using permutation-based inferential models. Differences were found between biomarker-positive and biomarker-negative sub-cohorts in global network metrics but, contrarily to our hypothesis, no differences were found in relation to episodic memory nodes. A significant node difference, however, was instead found in relation to category fluency (i.e. a test of semantic memory), with increased centrality observed among A+ participants. A similar, yet non-significant trend was also observed between A+T+ and A-T- participants. Network neuropsychology can complement and expand the study of cognitive performance carried out via 'traditional' univariate approaches. While univariate analyses reveal episodic memory decline in people with AD, this is not accompanied by any abnormalities at a neuropsychological network level. Our findings, however, highlight the importance of semantic memory alterations in A+ individuals. The wide set of neural and cognitive resources that sustain semantic memory may play a supportive role in the presence of neuropathology.

Triggering Receptor Expressed on Myeloid Cells 2 (TREM2) is a microglial receptor, sensitive to Phospholipids and Sphingomyelins, associated with neurodegeneration. Hypomorphic variants in the TREM2 gene significantly increase the risk of developing Alzheimer's disease (AD). The aim of this study was to characterize networks of lipids in post-mortem brain tissue from AD and Control donors, and to identify lipids associated with AD and impacted by dysfunctional TREM2. We studied human post-mortem brain tissue from the hippocampus and Brodmann area 9 (BA9) from 102 brains. Brain tissue from BA9 was available from n = 55 donors (14 Ad donors with a non-synonymous TREM2 risk variant [AD(TREM2+)], 20 Ad donors with no TREM2 risk variants [Ad(TREM2-)] and 21 Control donors), and brain tissue from the Hippocampus was available for n = 47 brain donors (7 Ad[TREM2+], 20 Ad[TREM2-] and 20 Control donors). Mass Spectrometry was performed to obtain lipidomic signatures spanning 99 lipid species that included the following lipid classes: Ceramides, Sphingomyelins, Phosphatidic acids, Phosphatidyl-cholines, Phosphatidyl-ethanolamines, Phosphatidyl-glycerols, Phosphatidyl-inositols, Phosphatidyl-serines and Triglycerides. Weighted gene co-expression network analysis (WGCNA) was used to identify highly correlated lipid modules and hubs in each brain region. Generalized least squares and linear regression analyses, adjusted for age at death, biological sex, number of Apolipoprotein E (APOE) ε4 alleles, and post-mortem delay, were used to assess the associations of lipid modules and hubs with AD and TREM2, in combined analyses across regions and in each region separately. Four lipid modules were relatively well-preserved between the two brain regions, and three of these modules were altered in AD donors and/or in AD TREM2 carriers. Levels of the BA9 'turquoise' module ('blue' hippocampus module), enriched in Sphingolipids and Phospholipids, were elevated in AD donors and particularly in AD TREM2 carriers [AD(TREM2+)]. The hub lipid of the BA9 'turquoise'/hippocampus 'blue' module, Phosphatidyl-serine [PS(32:1)], was increased in AD versus Control donors (beta = 0.677, 95% CI 0.28-1.08, P = 1.14E-03), and in AD(TREM2+) versus Control donors (beta = 1.00, 95% CI 0.53-1.48, P = 5.57E-03), whereas the strongest association was observed with Ceramide [Cer(d38:1)] increased in AD versus Control donors (beta = 0.929, 95% CI 0.46-1.40, P = 1.67E-04) and in AD(TREM2+) versus Controls donors (beta = 1.31, 95% CI 0.78-1.84, P = 4.35E-06). The consistent increase in TREM2 ligands such as Ceramides and Phosphatidyl-serines in the brains of AD donors, particularly TREM2 risk variants carriers, could reflect the presence of AD-associated damage signals in the form of stressed or apoptotic cells and damaged myelin.

The regulation of appetite by pharmaceuticals has gained significant interest for the treatment of obesity and cachexia. The melanocortin 4 receptor (MC4R) and the ghrelin receptor (GhrR) are known to play a crucial role in the regulation of energy homeostasis. Thus, peptide ligands, which modulate these receptors, have become attractive therapeutic lead structures. A key challenge is the efficient delivery of such peptides to the targeted receptors, which are expressed in the hypothalamus. Therefore, direct nose-to-brain delivery is a compelling strategy. Here, we report on food intake that is modulated by using intranasal applied peptides. We synthesized fluorescently labelled variants of the MC4R agonist setmelanotide, the GhrR agonist ghrelin (Ghr) and the GhrR inverse agonist KbFwLL-NH₂ [β-(3-benzothienyl)-D-alanine (b)] and assessed their receptor activity. Further, we measured the permeability and stability of these peptides on Calu-3 cells as a model system for the nasal mucosa. Next, the uptake of peptides after intranasal application was analysed in vivo by quantification of fluorescent signals in the olfactory bulb, cortex and hypothalamus. In addition, we monitored the effects of the two most promising peptides on food intake in vivo. Although no significant changes in body weight were observed, we detected differences in the daily change in food intake: this parameter was reduced for mice treated with setmelanotide variants and increased for mice treated with GhrR agonists compared to a control group. Taken together, our findings clearly underline the high potential of intranasal peptide administration for modulating food intake.

Brain age is a promising neuroimaging biomarker, reflecting biological aging, but long-term trajectories and predictive value for cognitive outcomes post-stroke remains unclear. This study aimed to characterize brain aging trajectories over 8 years following a first-ever stroke and to evaluate the predictive value of brain age estimates for long-term cognitive outcomes. We analysed data from working-age (<65 years) ischaemic stroke patients with small- and medium-sized strokes (lesion volumes <70 ml), using two longitudinal stroke cohorts. T1-weighted MRI was acquired in the acute phase and at multiple time points up to 8 years post-stroke. Montreal cognitive assessment (MoCA) was assessed at follow-up sessions. Brain age was estimated using a state-of-the-art deep learning model. Brain-predicted age difference (Brain-PAD) was calculated as estimated brain age minus chronological age and corrected by regressing on age, age² and sex. Linear mixed-effects models examined Brain-PAD over multiple time points (whole-brain, ipsilesional and contralesional). Normalized brain volume was derived from FreeSurfer and included in the whole-brain analysis. Linear regression models evaluated whether brain age was associated with cognitive performance (MoCA) at long-term follow-up. We included 120 patients [n = 50 (42%) female, mean ± SD age at discharge was 54.9 ± 9 and National Institutes of Health Stroke Scale was 3.7 ± 6.4], with a mean follow-up of 3.4 ± 2.5 years. The mean MoCA score at follow-up was 24.7 ± 3.7. Brain-PAD increased significantly over time in the whole-brain analysis (β = 0.6/year, P < 0.01), indicating 60% acceleration in brain aging after stroke, with the association remaining significant after adjusting for normalized brain volume (β = 0.5/year, P < 0.01). Accelerated brain aging was observed in the ipsilesional hemisphere (β = 0.7/year, P < 0.01), but not the contralesional hemisphere (β = 0.3/year, P = 0.12). Higher brain age in the acute phase of stroke predicted lower MoCA scores at follow-up (β = -0.12, P < 0.05), whereas chronological age was not a significant predictor (P = 0.12). The association between brain age estimations and cognitive performance remained significant after adjusting for age, sex and education (β = -0.42, P < 0.01). In this longitudinal study, we found accelerated brain aging following stroke. Furthermore, brain age was associated with cognitive outcomes several years later, highlighting its potential as an early biomarker for long-term cognitive prognosis.

Myotonic dystrophy type 1 is a dominantly inherited disorder, affecting musculoskeletal and central nervous systems and mainly characterized by progressive muscular atrophy and multisystemic damages including cardiac, respiratory and sleep dysfunctions. Neuroimaging studies conducted in myotonic dystrophy type 1 patients have documented widespread cerebral alterations encompassing structural, microstructural, functional and metabolic aspects of the brain, while comparatively few studies have investigated the role of iron concentration in the pathophysiology of central nervous system impairment. We report here the use of quantitative susceptibility (χ) mapping to explore iron content of both cortical and subcortical structures in myotonic dystrophy type 1 patients and to assess its possible clinical relevance, combining imaging and clinical data. Thirty-four myotonic dystrophy type 1 participants (20 females, 46.8 ± 12.0 years old) and 35 age- and sex- matched healthy controls (20 females, 50.5 ± 17.3 years old) were included in the study. All participants underwent MRI examinations in the same 3-Tesla scanner. The MRI protocol included 3D morphological T₁-weighted magnetization prepared rapid gradient echo and T₂*weighted multi-echo gradient echo for quantitative susceptibility mapping reconstruction. Cortical and subcortical structures were automatically segmented, and a volume of interest-based analysis was performed; χ distributions were compared between the two groups and myotonic dystrophy type 1 χ values were correlated with clinical and laboratory data. In the myotonic dystrophy type 1 group, a significant increase of χ was found in almost all cortical gyri, as a non-specific sign of neurodegeneration. Among subcortical structures, χ was significantly higher in myotonic dystrophy type 1 group in both thalamus (ventral and pulvinar nuclei) and brainstem (pons and medulla), compared to healthy controls. Additionally, correlation analysis showed some links between χ in subcortical structures and clinical signs, suggesting greater iron concentration with deterioration of clinical conditions. Thalamic χ values were associated with cardiological parameters and disability scores and, as with brainstem χ, they were also positively correlated with the number of central apnoeas; finally, thalamic and brainstem χ were negatively correlated with the age of onset. This study showed a correlation between autonomic dysfunction related to certain subcortical structures and their χ; higher values of χ correlated with greater functional impairment, suggesting iron accumulation detected by the quantitative susceptibility mapping technique is a possible biomarker of disease progression.

Alcohol use disorder (AUD) is a complex condition including affective, cognitive and motivational dimensions. Although AUD is known to induce diffuse brain damage, including grey matter shrinkage and ventricular enlargement, the microstructural changes it induces in white matter remain incompletely understood. This study leverages multi-shell diffusion MRI and multi-fixel models to (i) undertake whole-brain and tract-specific analyses to investigate the microstructure of white matter (WM) tracts affected by AUD, (ii) evaluate whether these differences persist in early abstinence, and (iii) correlate these results with clinical measures evaluated by validated psychological questionnaires. We recruited a final cohort of 37 AUD patients, admitted for alcohol withdrawal and selected for their ongoing alcohol consumption at the time of admission, and a demographically matched control group of 19 healthy subjects. Both groups underwent MRI scans at baseline and 18 days later, with assessments of depression, obsession-compulsion, and anxiety conducted in both sessions for the AUD patients and once for the control group. The imaging results confirmed the presence in AUD participants of clusters microstructural alterations in the fornix, corpus callosum, cingulum, uncinate fasciculus and anterior thalamic radiations. These white matter tracts presented global and localized microstructural changes in axial diffusivity and fractional anisotropy, which are linked to axonal damage and inflammation. There was no significant improvement in the diffusion metrics after almost three weeks of abstinence, although clinical measures did improve significantly. Depression scores were significantly elevated in the patients at admission and decreased with time. Depression scores before withdrawal showed correlations with microstructural metrics across the right anterior thalamic radiations, the isthmus of the corpus callosum, and the right uncinate fasciculus. Lower fractional anisotropy and higher radial diffusivity were predictive of higher depression scores. Overall, these findings highlight the long-term vulnerability of WM tracts affected by AUD and the link between tract microstructure, brain function and behaviour.