Arrow Research search

Author name cluster

L. Chen

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

11 papers
1 author row

Possible papers

11

YNIMG Journal 2022 Journal Article

Alpha power during task performance predicts individual language comprehension

  • P. Wang
  • Y. He
  • B. Maess
  • J. Yue
  • L. Chen
  • J. Brauer
  • A.D. Friederici
  • T.R. Knösche

Alpha power attenuation during cognitive task performing has been suggested to reflect a process of release of inhibition, increase of excitability, and thereby benefit the improvement of performance. Here, we hypothesized that changes in individual alpha power during the execution of a complex language comprehension task may correlate with the individual performance in that task. We tested this using magnetoencephalography (MEG) recorded during comprehension of German sentences of different syntactic complexity. Results showed that neither the frequency nor the power of the spontaneous oscillatory activity at rest were associated with the individual performance. However, during the execution of a sentences processing task, the individual alpha power attenuation did correlate with individual language comprehension performance. Source reconstruction localized these effects in left temporal-parietal brain regions known to be associated with language processing and their right-hemisphere homologues. Our results support the notion that in-task attenuation of individual alpha power is related to the essential mechanisms of the underlying cognitive processes, rather than merely to general phenomena like attention or vigilance.

Details DOI

YNICL Journal 2018 Journal Article

White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks

  • R. Guerrero
  • C. Qin
  • O. Oktay
  • C. Bowles
  • L. Chen
  • R. Joules
  • R. Wolz
  • M.C. Valdés-Hernández

White matter hyperintensities (WMH) are a feature of sporadic small vessel disease also frequently observed in magnetic resonance images (MRI) of healthy elderly subjects. The accurate assessment of WMH burden is of crucial importance for epidemiological studies to determine association between WMHs, cognitive and clinical data; their causes, and the effects of new treatments in randomized trials. The manual delineation of WMHs is a very tedious, costly and time consuming process, that needs to be carried out by an expert annotator (e.g. a trained image analyst or radiologist). The problem of WMH delineation is further complicated by the fact that other pathological features (i.e. stroke lesions) often also appear as hyperintense regions. Recently, several automated methods aiming to tackle the challenges of WMH segmentation have been proposed. Most of these methods have been specifically developed to segment WMH in MRI but cannot differentiate between WMHs and strokes. Other methods, capable of distinguishing between different pathologies in brain MRI, are not designed with simultaneous WMH and stroke segmentation in mind. Therefore, a task specific, reliable, fully automated method that can segment and differentiate between these two pathological manifestations on MRI has not yet been fully identified. In this work we propose to use a convolutional neural network (CNN) that is able to segment hyperintensities and differentiate between WMHs and stroke lesions. Specifically, we aim to distinguish between WMH pathologies from those caused by stroke lesions due to either cortical, large or small subcortical infarcts. The proposed fully convolutional CNN architecture, called uResNet, that comprised an analysis path, that gradually learns low and high level features, followed by a synthesis path, that gradually combines and up-samples the low and high level features into a class likelihood semantic segmentation. Quantitatively, the proposed CNN architecture is shown to outperform other well established and state-of-the-art algorithms in terms of overlap with manual expert annotations. Clinically, the extracted WMH volumes were found to correlate better with the Fazekas visual rating score than competing methods or the expert-annotated volumes. Additionally, a comparison of the associations found between clinical risk-factors and the WMH volumes generated by the proposed method, was found to be in line with the associations found with the expert-annotated volumes.

Details DOI

YNIMG Journal 2015 Journal Article

Evaluation of highly accelerated simultaneous multi-slice EPI for fMRI

  • L. Chen
  • A. T. Vu
  • J. Xu
  • S. Moeller
  • K. Ugurbil
  • E. Yacoub
  • D.A. Feinberg

Echo planar imaging (EPI) is the MRI technique that is most widely used for blood oxygen level-dependent (BOLD) functional MRI (fMRI). Recent advances in EPI speed have been made possible with simultaneous multi-slice (SMS) methods which combine acceleration factors M from multiband (MB) radiofrequency pulses and S from simultaneous image refocusing (SIR) to acquire a total of N= S × M images in one echo train, providing up to N times speed-up in total acquisition time over conventional EPI. We evaluated accelerations as high as N=48 using different combinations of S and M which allow for whole brain imaging in as little as 100ms at 3T with a 32 channel head coil. The various combinations of acceleration parameters were evaluated by tSNR as well as BOLD contrast-to-noise ratio (CNR) and information content from checkerboard and movie clips in fMRI experiments. We found that at low acceleration factors (N≤6), setting S =1 and varying M alone yielded the best results in all evaluation metrics, while at acceleration N=8 the results were mixed using both S =1 and S =2 sequences. At higher acceleration factors (N>8), using S =2 yielded maximal BOLD CNR and information content as measured by classification of movie clip frames. Importantly, we found significantly greater BOLD information content using relatively fast TRs in the range of 300ms–600ms compared to a TR of 2s, suggesting that faster TRs capture more information per unit time in task based fMRI.

Details DOI

YNIMG Journal 2012 Journal Article

The Human Connectome Project: A data acquisition perspective

  • D.C. Van Essen
  • K. Ugurbil
  • E. Auerbach
  • D. Barch
  • T.E.J. Behrens
  • R. Bucholz
  • A. Chang
  • L. Chen

The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.

Details DOI
v2026.09.13