Brain最新文献

Individuals with mild cognitive impairment (MCI) and an abnormal amyloid biomarker (A+) are at considerable increased risk of developing dementia. Still, these individuals vary greatly in rates of cognitive decline, and the mechanisms underlying this heterogeneity remain largely unclear. One factor related to an increased risk of progression to dementia is having an abnormal tau status (T+), but this still explains only part of the variance. Furthermore, previous work has indicated that MCI A+ individuals with T- or T+ are characterized by distinct molecular processes as reflected by distinct CSF proteomic profiles. As such, it could be hypothesized that differences in rates of cognitive decline in A+ MCI with abnormal or normal tau status may be explained by distinct underlying mechanisms. We studied this question using an untargeted CSF proteomic approach in individuals with MCI and abnormal amyloid. We measured untargeted Tandem Mass Tag (TMT) mass spectrometry proteomics in CSF of 80 A+ MCI individuals from the Amsterdam Dementia Cohort [age 66 ± 7.9 years, 52 (65%) T+]. For each protein, we tested if CSF levels were related to time to progression to dementia using Cox survival models; and with decline on the Mini-Mental State Examination (MMSE) with linear mixed models, correcting for age, sex and education. We validated our results in the independent Alzheimer's Disease Neuroimaging Initiative (ADNI) that employed the orthogonal CSF Soma logic protein measures in 245 CSF A+ MCI individuals [age 73 ± 7.2 years, 135 (55%) T+]. In total, we found 664 (29%) proteins to be related to cognitive decline in A+T+ and 718 (31%) proteins in A+T-. In A+T+, higher levels of 393 proteins that were associated with synaptic plasticity processes, and lower levels of 271 proteins associated with the immune function processes predicted a steeper decline on the MMSE and faster progression to dementia. In A+T-, higher levels of 306 proteins that were related to blood-brain barrier impairment and lower levels of 412 proteins associated with synaptic plasticity processes predicted a steeper decline; 67% of pathways associated with a decline in A+T+ and 58% in A+T- were replicated in ADNI. In conclusion, cognitive decline in A+ MCI individuals with and without tau may involve distinct underlying pathophysiology. These findings suggest that treatments aiming to delay cognitive decline may need tailoring according to the underlying mechanism of these patient groups, and that amyloid and tau levels could aid in stratification of selecting patients.

Multiple sclerosis (MS) is a highly heterogeneous disease in its clinical manifestation and progression. Predicting individual disease courses is key for aligning treatments with underlying pathobiology. We developed an unsupervised machine learning model integrating MRI-derived measures with serum neurofilament light chain (sNfL) levels to identify biologically informed MS subtypes and stages. Using a training cohort of patients with relapsing-remitting and secondary progressive MS (n = 189), with validation on a newly diagnosed population (n = 445), we discovered two distinct subtypes defined by the timing of sNfL elevation and MRI abnormalities (early- and late-sNfL types). In comparison to MRI-only models, incorporating sNfL with MRI improved correlations of data-derived stages with the Expanded Disability Status Scale in the training (Spearman's ρ = 0.420 versus MRI-only ρ = 0.231, P = 0.001) and external test sets (ρ = 0.163 for MRI-sNfL, versus ρ = 0.067 for MRI-only). The early-sNfL subtype showed elevated sNfL, corpus callosum injury and early lesion accrual, reflecting more active inflammation and neurodegeneration, whereas the late-sNfL group showed early volume loss in the cortical and deep grey matter volumes, with later sNfL elevation. Cross-sectional subtyping predicted longitudinal radiological activity: the early-sNfL group showed a 144% increased risk of new lesion formation (hazard ratio = 2.44, 95% confidence interval 1.38-4.30, P < 0.005) compared with the late-sNfL group. Baseline subtyping, over time, predicted treatment effect on new lesion formation on the external test set (faster lesion accrual in early-sNfL compared with late-sNfL, P = 0.01), in addition to treatment effects on brain atrophy (early sNfL average percentage brain volume change: -0.41, late-sNfL = -0.31, P = 0.04). Integration of sNfL provides an improved framework in comparison to MRI-only subtyping of MS to stage disease progression and inform prognosis. Our model predicted treatment responsiveness in early, more active disease states. This approach offers a powerful alternative to conventional clinical phenotypes and supports future efforts to refine prognostication and guide personalized therapy in MS.

Despite decades of development and clinical application, drug-resistant epilepsy occurs in 25%-30% of patients. One limiting factor in the success of antiseizure medications are challenges in mapping the neural effects of epilepsy drugs to seizure mechanisms in humans. Most antiseizure medications were developed in animal models and primarily target nano-scale structures like ion channels and receptors. However, they exert their effects and are typically measured in humans at the macro-scale using techniques like EEG and conventional functional MRI (fMRI). This disconnect between the mechanisms of pharmaceutical interventions and the clinical management of epilepsy leaves a critical gap in our understanding. This is because all seizures, even those of a generalized nature, appear to initiate in intermediate scale, local microcircuits and then propagate from that initial ictogenic zone. Invasive electrophysiological recordings in both animal models and humans have shown that one such microcircuit, cortical layers, and more specifically deep cortical layers, play a critical role in seizure generation in both generalized and focal epilepsies, serving as the critical link between nano-scale dysfunctions and the macro-scale activity observed in seizures. Laminar fMRI, a technique capable of resolving activity across cortical depths, offers a promising avenue to bridge this gap. By providing a non-invasive measure of laminar response alterations in humans, it could complement animal model and electrophysiological findings, offering novel insights into the layer-specific mechanisms of seizure generation and propagation in humans. This review discusses evidence for this concept, highlighting key findings from animal models and human intracranial recordings in this regard, and details how laminar fMRI may be able to refine our understanding of epilepsy at the microcircuit level. It concludes with a discussion regarding the possible role of laminar fMRI in improving surgical targeting for focal epilepsies, elucidating the mechanistic effects of antiseizure medications, and ultimately, targeting current and future epilepsy treatments.