Biology subjects

Jäncke, L.

Publications and source records attributed to Jäncke, L..

4 recordsLinked to original sources

bioRxiv2020

Validation and comparison of three freely available methods for extracting white matter hyperintensities: FreeSurfer, UBO Detector and BIANCA

White matter hyperintensities (WMH) of presumed vascular origin are frequently found in MRIs of healthy older adults. WMH are also associated with aging and cognitive decline. Here, we compared and validated three freely available algorithms for WMH extraction: FreeSurfer, UBO Detector, and BIANCA (Brain Intensity AbNormality Classification Algorithm) using a longitudinal dataset comprising MRI data of cognitively healthy older adults (baseline N = 231, age range 64-87 years). As reference we manually segmented WMH in T1w, 3D FLAIR, 2D FLAIR images. These manual segmentations were then used to assess the segmentation accuracy of the different automated algorithms. Further, we assessed the relationships of WMH volume estimates provided by the algorithms with Fazekas scores and age. FreeSurfer underestimated the WMH volumes and scored worst in Dice Similarity Coefficient (DSC = 0.434) but its WMH volumes strongly correlated with the Fazekas scores (rs = 0.73). BIANCA accomplished the highest DCS (0.602) with 3D FLAIR images. However, the relations with the Fazekas scores were only moderate, especially in the 2D FLAIR images (rs = 0.41), and many outlier WMH volumes were detected when exploring within-person trajectories (2D FLAIR: ~30%). UBO Detector performed similarly to BIANCA in DSC with both modalities and reached the best DSC in 2D FLAIR (0.531) without requiring a tailored training dataset. In addition, it achieved very high associations with the Fazekas scores (2D FLAIR: rs = 0.80). In summary, our results emphasize the importance of carefully contemplating the choice of the WMH segmentation algorithm and MR-modality.

neuroscience↗

bioRxiv2020

Heterogeneity of EEG resting-state brain networks in absolute pitch

The neural basis of absolute pitch (AP), the ability to effortlessly identify a musical tone without an external reference, is poorly understood. One of the key questions is whether perceptual or cognitive processes underlie the phenomenon as both sensory and higher-order brain regions have been associated with AP. One approach to elucidate the neural underpinnings of a specific expertise is the examination of resting-state networks. Thus, in this paper, we report a comprehensive functional network analysis of intracranial resting-state EEG data in a large sample of AP musicians (n = 54) and non-AP musicians (n = 51). We adopted two analysis approaches: First, we applied an ROI-based analysis to examine the connectivity between the auditory cortex and the dorsolateral prefrontal cortex (DLPFC) using several established functional connectivity measures. This analysis is a replication of a previous study which reported increased connectivity between these two regions in AP musicians. Second, we performed a whole-brain network-based analysis on the same functional connectivity measures to gain a more complete picture of the brain regions involved in a possibly large-scale network supporting AP ability. In our sample, the ROI-based analysis did not provide evidence for an AP-specific connectivity increase between the auditory cortex and the DLPFC. In contrast, the whole-brain analysis revealed three networks with increased connectivity in AP musicians comprising nodes in frontal, temporal, subcortical, and occipital areas. Commonalities of the networks were found in both sensory and higher-order brain regions of the perisylvian area. Further research will be needed to confirm these exploratory results.

neuroscience↗

bioRxiv2019

10Kin1day: A bottom-up neuroimaging initiative

We organized 10Kin1day, a pop-up scientific event with the goal to bring together neuroimaging groups from around the world to jointly analyze 10,000+ existing MRI connectivity datasets during a 3-day workshop. In this report, we describe the motivation and principles of 10Kin1day, together with a public release of 8,000+ MRI connectome maps of the human brain.

neuroscience↗

bioRxiv2018

Dissociating refreshing and elaboration by their neural signatures and their effects on memory in young and old adults

Maintenance of information in working memory (WM) is assumed to rely on refreshing and elaboration, but clear mechanistic descriptions of these cognitive processes are lacking, and it is unclear whether they are simply two labels for the same process. This fMRI study investigated the extent to which refreshing, elaboration, and repeating of items in WM are distinct neural processes with dissociable behavioral outcomes in WM and long-term memory (LTM). Multivariate pattern analyses of fMRI data revealed differentiable neural signatures for these processes, which we also replicated in an independent sample of older adults. In some cases, the degree of neural separation within an individual predicted their memory performance. Elaboration improved LTM, but not WM, and this benefit increased as its neural signature became more distinct from repetition. Refreshing had no impact on LTM, but did improve WM, although the neural discrimination of this process was not predictive of the degree of improvement. These results demonstrate that refreshing and elaboration are separate processes that differently contribute to memory performance.\n\nHighlightsO_LIRepeated reading, refreshing, and elaboration are differentiable in brain activation patterns in both young and older adults.\nC_LIO_LIElaboration selectively improved long-term memory for young adults, and the size of the benefit was related to the neural separability of elaboration from other processes.\nC_LIO_LIOlder adults implemented a sub-optimal form of elaboration, and this may be a factor contributing to age-related deficits in long-term memory.\nC_LI\n\nEthics statementThe study was approved by the ethical review board of the canton of Zurich (BASEC-No. 2017-00190) and all subjects gave informed written consent in accordance with the Declaration of Helsinki.\n\nData and code availability statementAll behavioral data and analysis scripts can be assessed on the Open Science Framework (osf.io/p2h8b/). The fMRI data that support the findings of this study are available on request from the corresponding author, LMB. The fMRI data are not publicly available due to restrictions of the Swiss Ethics Committees on research involving humans regarding data containing information that could compromise the privacy of research participants.

neuroscience↗