Multi-Night Electroencephalography Reveals Positive Association Between Sleep Efficiency and Hippocampal Subfield and Entorhinal Cortex Volumes in Healthy Aging.

Age-related atrophy of the human hippocampus and the enthorinal cortex starts accelerating at around age 60. Due to the contributions of these regions to many cognitive functions seamlessly used in everyday life, this can heavily impact the lives of elderly people. The hippocampus is not a unitary structure, and mechanisms of its age-related decline appear to differentially affect its subfields.

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiologic Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiologic assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and image quality metrics during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.

Operatic voices engage the default mode network in professional opera singers.

Extensive research with musicians has shown that instrumental musical training can have a profound impact on how acoustic features are processed in the brain. However, less is known about the influence of singing training on neural activity during voice perception, particularly in response to salient acoustic features, such as the vocal vibrato in operatic singing. To address this gap, the present study employed functional magnetic resonance imaging (fMRI) to measure brain responses in trained opera singers and musically untrained controls listening to recordings of opera singers performing in two distinct styles: a full operatic voice with vibrato, and a straight voice without vibrato.

Contribution of the lateral occipital and parahippocampal cortices to pattern separation of objects and contexts.

Contextual features are integral to episodic memories; yet, we know little about context effects on pattern separation, a hippocampal function promoting orthogonalization of overlapping memory representations. Recent studies suggested that various extrahippocampal brain regions support pattern separation; however, the specific role of the parahippocampal cortex-a region involved in context representation-in pattern separation has not yet been studied. Here, we investigated the contribution of the parahippocampal cortex (specifically, the parahippocampal place area) to context reinstatement effects on mnemonic discrimination, using functional magnetic resonance imaging.

The effect of head motion on brain age prediction using deep convolutional neural networks.

Deep learning can be used effectively to predict participants' age from brain magnetic resonance imaging (MRI) data, and a growing body of evidence suggests that the difference between predicted and chronological age-referred to as brain-predicted age difference (brain-PAD)-is related to various neurological and neuropsychiatric disease states. A crucial aspect of the applicability of brain-PAD as a biomarker of individual brain health is whether and how brain-predicted age is affected by MR image artifacts commonly encountered in clinical settings. To investigate this issue, we trained and validated two different 3D convolutional neural network architectures (CNNs) from scratch and tested the models on a separate dataset consisting of motion-free and motion-corrupted T1-weighted MRI scans from the same participants, the quality of which were rated by neuroradiologists from a clinical diagnostic point of view.