Independent component analysis (ICA) is a powerful method for source separation and has been used for decomposition of EEG, MRI, and concurrent EEG-fMRI data. ICA is not naturally suited to draw group inferences since it is a non-trivial problem to identify and order components across individuals. One solution to this problem is to create aggregate data containing observations from all subjects, estimate a single set of components and then back-reconstruct this in the individual data. Here, we describe such a group-level temporal ICA model for event related EEG. When used for EEG time series analysis, the accuracy of component detection and back-reconstruction with a group model is dependent on the degree of intra- and interindividual time and phase-locking of event related EEG processes. We illustrate this dependency in a group analysis of hybrid data consisting of three simulated event-related sources with varying degrees of latency jitter and variable topographies. Reconstruction accuracy was tested for temporal jitter 1, 2 and 3 times the FWHM of the sources for a number of algorithms. The results indicate that group ICA is adequate for decomposition of single trials with physiological jitter, and reconstructs event related sources with high accuracy.

Our understanding of the cognitive functions of the human brain has tremendously benefited from the population functional Magnetic Resonance Imaging (fMRI) studies in the last three decades. The reliability and replicability of the fMRI results, however, have been recently questioned, which has been named the replication crisis. Sufficient statistical power is fundamental to alleviate the crisis, by either “going big,” leveraging big datasets, or by “going small,” densely scanning several participants. Here we reported a “going small” project implemented in our department, the Bergen breakfast scanning club (BBSC) project, in which three participants were intensively scanned across a year. It is expected this kind of new data collection method can provide novel insights into the variability of brain networks, facilitate research designs and inference, and ultimately lead to the improvement of the reliability of the fMRI results.

Introduction: Cognitive aging is associated with a decline on measures of fluid intelligence (gF), whereas crystallized intelligence (gC) tends to remain stable. In the present study we asked if depressive symptoms might contribute to explain the decline on gF in a sample of healthy middle-aged and older adults. Method: The Norwegian sample included 83 females and 42 males (M = 60, SD = 7.9 years). gF was calculated from factor-analysis, including tests of matrix reasoning (WASI), memory function (CVLT-II), processing speed and executive function (CDT; CWIT). gC was derived from a Vocabulary subtest (WASI). Depressive symptoms were assessed by self-reports on Beck’s Depression Index (BDI) and ranged from 0 to 21 (M = 6, SD = 4.5). Results: Increased age was correlated with a decline on gF (r = −0.436, p  < 0.001), but not gC (r=−0.103, p = ns.). The BDI score in the whole sample was correlated with gF (r = −0.313, p < 0.001). A more detailed analysis showed that the BDI score correlated with measures of both gF and gC in males. The correlations were non-significant for females on all measures, with the exception of a measure of processing speed/executive function. A regression analysis including age and sex in the first step, showed that symptoms of depression significantly contributed to explain decline on gF, F(3, 124) = 16.653, p < 0.001, R? = 0.292, ΔR? = 0.054. Discussion: The results showed that symptoms of depression were negatively correlated with cognitive functioning in males even when the symptom-level was below clinical threshold. This indicates that minimal symptoms of depression in older men are clinically relevant to address.

Populational brain imaging methods based on group averages provide valuable insights into the general functions of the brain. However, they often overlook the inherent inter- and intra-subject variability, limiting our understanding of individual differences. To address this limitation, researchers have turned to big datasets and deep brain imaging datasets. Big datasets enable the exploration of inter-subject variations, while deep brain imaging datasets, involving repeated scanning of multiple subjects over time, offer detailed insights into intra-subject variability. Despite the availability of numerous big datasets, the number of deep brain imaging datasets remains limited. In this article, we present a deep brain imaging dataset derived from the Bergen Breakfast Scanning Club (BBSC) project. The dataset comprises data collected from three subjects who underwent repeated scanning over the course of approximately one year. Specifically, three types of data chunks were collected: behavioral data, functional brain data, and structural brain data. Functional brain images, encompassing magnetic resonance spectroscopy (MRS) and resting-state functional magnetic resonance imaging (fMRI), along with their anatomical reference T1-weighted brain images, were collected twice a week during the data collection period. In total, 38, 40, and 25 sessions of functional data were acquired for subjects 1, 2, and 3, respectively. On the other hand, structural brain images, including T2-weighted brain images, diffusion-weighted images (DWI), and fluid-attenuated inversion recovery (FLAIR) images, were obtained once a month. A total of 10, 9, and 6 sessions were collected for subjects 1, 2, and 3, respectively. The primary objective of this article is to provide a comprehensive description of the data acquisition protocol employed in the BBSC project, as well as detailed insights into the preprocessing steps applied to the acquired data.

Variability in brain age predictions complicate the metric's clinical usage, for example, as a (pre-) diagnostic tool. We presented small correlations between age and brain age when repeatedly sampling T1-weighted MRI data from the same individual in a short period of time (1-3 years). Reasons might lay in the absence of maturation effects for the age range in the presented sample, brain age model-bias (including a bimodal or trimodal age training distribution) and model error. We also find evidence for the influence of field strength and scan quality on brain age. Individual differences and the processing of such in the brain age model, might lead to variability in associations between brain age and QC metrics. The presented testing of an established brain age model on multiple single-subject short-timespan retesting data is a stricter test than the usual use-case and does not invalidate MRI group differences. However, intra-individual differences contributing to brain age require further attention in order to advance brain age as a clinical tool.