Billegomez2288

Estimated rate of change curves for FA, MD, and RD exhibited an initial 10-week period of exceedingly rapid WM development, followed by a precipitous decline in growth rates. K-means clustering of raw DTI trajectories and rank ordering of LME model parameters revealed distinct posterior-to-anterior and medial-to-lateral gradients in WM maturation. Finally, we found that individual differences in WM microstructure assessed at 3 weeks of age were significantly related to those at 1 year of age. This study provides a quantitative characterization of very early WM growth in NHPs and lays the foundation for future work focused on the impact of alterations in early WM developmental trajectories in relation to human psychopathology.Brain age prediction studies aim at reliably estimating the difference between the chronological age of an individual and their predicted age based on neuroimaging data, which has been proposed as an informative measure of disease and cognitive decline. As most previous studies relied exclusively on magnetic resonance imaging (MRI) data, we hereby investigate whether combining structural MRI with functional magnetoencephalography (MEG) information improves age prediction using a large cohort of healthy subjects (N = 613, age 18-88 years) from the Cam-CAN repository. To this end, we examined the performance of dimensionality reduction and multivariate associative techniques, namely Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA), to tackle the high dimensionality of neuroimaging data. Using MEG features (mean absolute error (MAE) of 9.60 years) yielded worse performance when compared to using MRI features (MAE of 5.33 years), but a stacking model combining both feature sets improved age prediction performance (MAE of 4.88 years). Furthermore, we found that PCA resulted in inferior performance, whereas CCA in conjunction with Gaussian process regression models yielded the best prediction performance. Firsocostat research buy Notably, CCA allowed us to visualize the features that significantly contributed to brain age prediction. We found that MRI features from subcortical structures were more reliable age predictors than cortical features, and that spectral MEG measures were more reliable than connectivity metrics. Our results provide an insight into the underlying processes that are reflective of brain aging, yielding promise for the identification of reliable biomarkers of neurodegenerative diseases that emerge later during the lifespan.

As the population ages, maintaining mental health and well-being of older adults is a public health priority. Beyond objective measures of health, self-perceived quality of life (QoL) is a good indicator of successful aging. In older adults, it has been shown that QoL is related to structural brain changes. However, QoL is a multi-faceted concept and little is known about the specific relationship of each QoL domain to brain structure, nor about the links with other aspects of brain integrity, including white matter microstructure, brain perfusion and amyloid deposition, which are particularly relevant in aging. Therefore, we aimed to better characterize the brain biomarkers associated with each QoL domain using a comprehensive multimodal neuroimaging approach in older adults.

One hundred and thirty-five cognitively unimpaired older adults (mean age±SD 69.4±3.8 y) underwent structural and diffusion magnetic resonance imaging, together with early and late florbetapir positron emission tomography scans. QoLsality of the relationships between QoL and brain integrity.Neural networks involved in placebo analgesia and nocebo hyperalgesia processes have been widely investigated with neuroimaging methods. However, few studies have directly compared these two processes and it remains unclear whether common or distinct neural circuits are involved. To address this issue, we implemented a coordinate-based meta-analysis and compared neural representations of placebo analgesia (30 studies; 205 foci; 677 subjects) and nocebo hyperalgesia (22 studies; 301 foci; 401 subjects). Contrast analyses confirmed placebo-specific concordance in the right ventral striatum, and nocebo-specific concordance in the dorsal anterior cingulate cortex (dACC), left posterior insula and left parietal operculum during combined pain anticipation and administration stages. Importantly, no overlapping regions were found for these two processes in conjunction analyses, even when the threshold was low. Meta-analytic connectivity modeling (MACM) and resting-state functional connectivity (RSFC) analyses on key regions further confirmed the distinct brain networks underlying placebo analgesia and nocebo hyperalgesia. Together, these findings indicate that the placebo analgesia and nocebo hyperalgesia processes involve distinct neural circuits, which supports the view that the two phenomena may operate via different neuropsychological processes.The General Linear Model (GLM) used in task-fMRI relates activated brain areas to extrinsic task conditions. The translation of resulting neural activation into a hemodynamic response is commonly approximated with a linear convolution model using a hemodynamic response function (HRF). There are two major limitations in GLM analysis. Firstly, the GLM assumes that neural activation is either on or off and matches the exact stimulus duration in the corresponding task timings. Secondly, brain networks observed in resting-state fMRI experiments present also during task experiments, but the GLM approach models these task-unrelated brain activity as noise. A novel kernel matrix factorization approach, called hemodynamic matrix factorization (HMF), is therefore proposed that addresses both limitations by assuming that task-related and task-unrelated brain activity can be modeled with the same convolution model as in GLM analysis. By contrast to the GLM, the proposed HMF is a blind source separation (BSS) technique, w HMF also produced seemingly task-unrelated modes whose spatial maps matched known resting-state networks. The validity of a fMRI task experiment relies on the assumption that the exposure to a stimulus for a given time causes an imminent increase in neural activation of equal duration. The proposed HMF is an attempt to falsify this assumption and allows to identify subject task participation that does not comply with the experiment instructions.