Most cited article - PubMed ID 30403309
Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies-Quantifying noise removal and neural signal preservation
The study evaluates the efficacy of RETROICOR (Retrospective Image Correction) in mitigating physiological artifacts within multi-echo (ME) fMRI data. Two RETROICOR implementations were compared: applying corrections to individual echoes (RTC_ind) versus composite multi-echo data (RTC_comp). Data from 50 healthy participants were collected using diverse acquisition parameters, including multiband acceleration factors and varying flip angles, on a Siemens Prisma 3T scanner. Key metrics such as temporal signal-to-noise ratio (tSNR), signal fluctuation sensitivity (SFS), and variance of residuals demonstrated improved data quality in both RETROICOR models, particularly in moderately accelerated runs (multiband factors 4 and 6) with lower flip angles (45°). Differences between RTC_ind and RTC_comp were minimal, suggesting both methods are viable for practical applications. While the highest acceleration (multiband factor 8) degraded data quality, RETROICOR's compatibility with faster acquisition sequences was confirmed. These findings underscore the importance of optimizing acquisition parameters and noise correction techniques for reliable fMRI investigations.
- Keywords
- RETROICOR, denoising, fMRI, multi‐echo,
- MeSH
- Artifacts * MeSH
- Adult MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods MeSH
- Brain Mapping * methods MeSH
- Young Adult MeSH
- Brain * diagnostic imaging physiology MeSH
- Image Processing, Computer-Assisted * methods MeSH
- Signal-To-Noise Ratio MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
- MeSH
- Acetylcholine metabolism MeSH
- Default Mode Network physiology diagnostic imaging MeSH
- Adult MeSH
- Connectome * MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Models, Neurological * MeSH
- Brain * physiology diagnostic imaging MeSH
- Nerve Net physiology diagnostic imaging MeSH
- Rest * physiology MeSH
- Computer Simulation MeSH
- Computational Biology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Male MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Acetylcholine MeSH
We wanted to verify the effect of combining multi-echo (ME) functional magnetic resonance imaging (fMRI) with slice acceleration in simultaneous multi-slice acquisition. The aim was to shed light on the benefits of multiple echoes for various acquisition settings, especially for levels of slice acceleration and flip angle. Whole-brain ME fMRI data were obtained from 26 healthy volunteers (using three echoes; seven runs with slice acceleration 1, 4, 6, and 8; and two different flip angles for each of the first three acceleration factors) and processed as single-echo (SE) data and ME data based on optimal combinations weighted by the contrast-to-noise ratio. Global metrics (temporal signal-to-noise ratio, signal-to-noise separation, number of active voxels, etc.) and local characteristics in regions of interest were used to evaluate SE and ME data. ME results outperformed SE results in all runs; the differences became more apparent for higher acceleration, where a significant decrease in data quality is observed. ME fMRI can improve the observed data quality metrics over SE fMRI for a wide range of accelerated fMRI acquisitions.
- Keywords
- BOLD, TE dependence, acquisition acceleration, multi-echo fMRI, simultaneous multi-slice imaging,
- MeSH
- Adult MeSH
- Echo-Planar Imaging methods MeSH
- Globus Pallidus diagnostic imaging physiology MeSH
- Humans MeSH
- Magnetic Resonance Imaging methods MeSH
- Brain Mapping methods MeSH
- Young Adult MeSH
- Cerebral Cortex diagnostic imaging physiology MeSH
- Image Processing, Computer-Assisted methods MeSH
- Psychomotor Performance physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
INTRODUCTION: According to the strong version of the orthographic depth hypothesis, in languages with transparent letter-sound mappings (shallow orthographies) the reading of both familiar words and unfamiliar nonwords may be accomplished by a sublexical pathway that relies on serial grapheme-to-phoneme conversion. However, in languages such as English characterized by inconsistent letter-sound relationships (deep orthographies), word reading is mediated by a lexical-semantic pathway that relies on mappings between word-specific orthographic, semantic, and phonological representations, whereas the sublexical pathway is used primarily to read nonwords. METHODS: In this study, we used functional magnetic resonance imaging to elucidate neural substrates of reading in Czech, a language characterized by a shallo worthography. Specifically, we contrasted patterns of brain activation and connectivity during word and nonword reading to determine whether similar or different neural mechanisms are involved. Neural correlates were measured as differences in simple whole-brain voxel-wise activation, and differences in visual word form area (VWFA) task-related connectivity were computed on the group level from data of 24 young subject. Trial-to-trial reading reaction times were used as a measure of task difficulty, and these effects were subtracted from the activation and connectivity effects in order to eliminate difference in cognitive effort which is naturally higher for nonwords and may mask the true lexicality effects. RESULTS: We observed pattern of activity well described in the literature mostly derived from data of English speakers - nonword reading (as compared to word reading) activated the sublexical pathway to a greater extent whereas word reading was associated with greater activation of semantic networks. VWFA connectivity analysis also revealed stronger connectivity to a component of the sublexical pathway - left inferior frontal gyrus (IFG), for nonword compared to word reading. DISCUSSION: These converging results suggest that the brain mechanism of skilled reading in shallow orthography languages are similar to those engaged when reading in languages with a deep orthography and are supported by a universal dual-pathway neural architecture.
- Keywords
- VWFA, fMRI, lexical-semantic, phonology, reading, shallow orthography, visual word form area,
- Publication type
- Journal Article MeSH
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ 4 band and low β 1 band) demonstrated significant negative linear relationship (p FWE < 0.05) to the frequent stimulus and three patterns (two low δ 2 and δ 3 bands, and narrow θ 1 band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ 4 model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ 4 model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β 1 patterns visualized less significant and distinct suprathreshold spatial associations. Each θ 1 model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ 1 model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ 4, β 1, and θ 1 bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM.
Functional connectivity analysis of resting-state fMRI data has recently become one of the most common approaches to characterizing individual brain function. It has been widely suggested that the functional connectivity matrix is a useful approximate representation of the brain's connectivity, potentially providing behaviorally or clinically relevant markers. However, functional connectivity estimates are known to be detrimentally affected by various artifacts, including those due to in-scanner head motion. Moreover, as individual functional connections generally covary only very weakly with head motion estimates, motion influence is difficult to quantify robustly, and prone to be neglected in practice. Although the use of individual estimates of head motion, or group-level correlation of motion and functional connectivity has been suggested, a sufficiently sensitive measure of individual functional connectivity quality has not yet been established. We propose a new intuitive summary index, Typicality of Functional Connectivity, to capture deviations from standard brain functional connectivity patterns. In a resting-state fMRI dataset of 245 healthy subjects, this measure was significantly correlated with individual head motion metrics. The results were further robustly reproduced across atlas granularity, preprocessing options, and other datasets, including 1,081 subjects from the Human Connectome Project. In principle, Typicality of Functional Connectivity should be sensitive also to other types of artifacts, processing errors, and possibly also brain pathology, allowing extensive use in data quality screening and quantification in functional connectivity studies as well as methodological investigations.
- Keywords
- atlas, functional connectivity, motion, quality, rs-fMRI,
- MeSH
- Artifacts MeSH
- Atlases as Topic * MeSH
- Datasets as Topic * MeSH
- Adult MeSH
- Head Movements MeSH
- Connectome * methods standards MeSH
- Humans MeSH
- Magnetic Resonance Imaging * methods standards MeSH
- Young Adult MeSH
- Brain diagnostic imaging physiology MeSH
- Image Processing, Computer-Assisted * methods standards MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH
This study investigates the role of the dorsal/sensorimotor striatum in visuomotor integration (i.e., the transformation of internal visual information about letter shapes into motor output) during handwriting. Twenty healthy participants underwent fMRI scanning with tasks consisting of self-paced handwriting of alphabetically ordered single letters and simple dots, with both tasks performed without visual feedback. Functional connectivity (FC) from these two tasks was compared to demonstrate the difference between coordinated activity arising during handwriting and the activity during a simpler motor condition. Our study focused upon the writing-specific cortico-striatal network of preselected regions of interest consisting of the visual word form area (VWFA), anterior intraparietal sulcus/superior parietal lobule, striatum, premotor cortex/Exner's area, and primary and supplementary motor regions. We observed systematically increased task-induced cortico-striatal and cortico-cortical FC. This increased synchronization of neural activity between the VWFA, i.e., the visual cortical area containing information about letter shapes, and the frontoparietal motor regions is mediated by the striatum. These findings suggest the involvement of the striatum in integrating stored letter-shape information with motor planning and execution during handwriting.
- Keywords
- Basal ganglia, Functional connectivity, Handwriting, Striatum, Visuomotor integration, fMRI,
- MeSH
- Corpus Striatum diagnostic imaging physiology MeSH
- Adult MeSH
- Humans MeSH
- Magnetic Resonance Imaging MeSH
- Young Adult MeSH
- Cerebral Cortex diagnostic imaging physiology MeSH
- Nerve Net diagnostic imaging physiology MeSH
- Motor Activity physiology MeSH
- Handwriting * MeSH
- Psychomotor Performance physiology MeSH
- Pattern Recognition, Visual physiology MeSH
- Check Tag
- Adult MeSH
- Humans MeSH
- Young Adult MeSH
- Male MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
- Research Support, Non-U.S. Gov't MeSH