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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., (© 2021 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Parcellation-based approaches are an important part of functional magnetic resonance imaging data analysis. They are a necessary processing step for sorting data in structurally or functionally homogenous regions. Real functional magnetic resonance imaging datasets usually do not cover the atlas template completely; they are often spatially constrained due to the physical limitations of MR sequence settings, the inter-individual variability in brain shape, etc. When using a parcellation template, many regions are not completely covered by actual data. This paper addresses the issue of the area coverage required in real data in order to reliably estimate the representative signal and the influence of this kind of data loss on network analysis metrics. We demonstrate this issue on four datasets using four different widely used parcellation templates. We used two erosion approaches to simulate data loss on the whole-brain level and the ROI-specific level. Our results show that changes in ROI coverage have a systematic influence on network measures. Based on the results of our analysis, we recommend controlling the ROI coverage and retaining at least 60% of the area in order to ensure at least 80% of explained variance of the original signal.

Background: In some fields of fMRI data analysis, using correct methods for dealing with noise is crucial for achieving meaningful results. This paper provides a quantitative assessment of the effects of different preprocessing and noise filtering strategies on psychophysiological interactions (PPI) methods for analyzing fMRI data where noise management has not yet been established., Methods: Both real and simulated fMRI data were used to assess these effects. Four regions of interest (ROIs) were chosen for the PPI analysis on the basis of their engagement during two tasks. PPI analysis was performed for 32 different preprocessing and analysis settings, which included data filtering with RETROICOR or no such filtering; different filtering of the ROI "seed" signal with a nuisance data-driven time series; and the involvement of these data-driven time series in the subsequent PPI GLM analysis. The extent of the statistically significant results was quantified at the group level using simple descriptive statistics. Simulated data were generated to assess statistical improvement of different filtering strategies., Results: We observed that different approaches for dealing with noise in PPI analysis yield differing results in real data. In simulated data, we found RETROICOR, seed signal filtering and the addition of data-driven covariates to the PPI design matrix significantly improves results., Conclusions: We recommend the use of RETROICOR, and data-driven filtering of the whole data, or alternatively, seed signal filtering with data-driven signals and the addition of data-driven covariates to the PPI design matrix., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: This study investigates the modulatory effect of stimulus sequence on neural responses to novel stimuli. A group of 34 healthy volunteers underwent event-related functional magnetic resonance imaging while performing a three-stimulus visual oddball task, involving randomly presented frequent stimuli and two types of infrequent stimuli - targets and distractors., New Method: We developed a modified categorization of rare stimuli that incorporated the type of preceding rare stimulus, and analyzed the event-related functional data according to this sequence categorization; specifically, we explored hemodynamic response modulation associated with increasing rare-to-rare stimulus interval., Results: For two consecutive targets, a modulation of brain function was evident throughout posterior midline and lateral temporal cortex, while responses to targets preceded by distractors were modulated in a widely distributed fronto-parietal system. As for distractors that follow targets, brain function was modulated throughout a set of posterior brain structures. For two successive distractors, however, no significant modulation was observed, which is consistent with previous studies and our primary hypothesis., Comparison With Existing Methods: The addition of the aforementioned technique extends the possibilities of conventional oddball task analysis, enabling researchers to explore the effects of the whole range of rare stimuli intervals., Conclusion: This methodology can be applied to study a wide range of associated cognitive mechanisms, such as decision making, expectancy and attention., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Changes in connectivity of the posterior node of the default mode network (DMN) were studied when switching from baseline to a cognitive task using functional magnetic resonance imaging. In all, 15 patients with mild to moderate Alzheimer's disease (AD) and 18 age-, gender-, and education-matched healthy controls (HC) participated in the study. Psychophysiological interactions analysis was used to assess the specific alterations in the DMN connectivity (deactivation-based) due to psychological effects from the complex visual scene encoding task. In HC, we observed task-induced connectivity decreases between the posterior cingulate and middle temporal and occipital visual cortices. These findings imply successful involvement of the ventral visual pathway during the visual processing in our HC cohort. In AD, involvement of the areas engaged in the ventral visual pathway was observed only in a small volume of the right middle temporal gyrus. Additional connectivity changes (decreases) in AD were present between the posterior cingulate and superior temporal gyrus when switching from baseline to task condition. These changes are probably related to both disturbed visual processing and the DMN connectivity in AD and reflect deficits and compensatory mechanisms within the large scale brain networks in this patient population. Studying the DMN connectivity using psychophysiological interactions analysis may provide a sensitive tool for exploring early changes in AD and their dynamics during the disease progression.

Understanding the neural systems underpinning social cognition is a primary focus of contemporary social neuroscience. Using functional magnetic resonance imaging (fMRI), the present study asked if brain activity reflecting socio-cognitive processes differs between individuals according to their social behavior; namely, between a group of drivers with frequent traffic offenses and a group with none. Socio-cognitive processing was elicited by employing videos from a traffic awareness campaign, consisting of reckless and anti-social driving behavior ending in tragic consequences, and control videos with analogous driving themes but without such catastrophic endings. We investigated whether relative increases in brain function during the observation of these campaign stimuli compared with control videos differed between these two groups. To develop the results of our previous study we focused our analyses on superior temporal sulcus/gyrus (STS/STG). This revealed a bigger increase in brain activity within this region during the campaign stimuli in safe compared with dangerous drivers. Furthermore, by thematically coding drivers' verbal descriptions of the stimuli, we also demonstrate differences in STS reactivity according to drivers' scores on two indices of socio-cognitive processing: subjects' perceived consequences of actors' actions, and their affective evaluation of the clips. Our results demonstrate the influence of social behavior and socio-cognitive processing on STS reactivity to social stimuli, developing considerably our understanding of the role of this region in social cognition., (© 2013.)

Background and Purpose: The cerebellum, basal ganglia (BG), and other cortical regions, such as supplementary motor area (SMA) have emerged as important structures dealing with various aspects of timing, yet the modulation of functional connectivity between them during motor timing tasks remains unexplored., Methods: We used dynamic causal modeling to investigate the differences in effective connectivity (EC) between these regions and its modulation by behavioral outcome during a motor timing prediction task in a group of 16 patients with early Parkinson's disease (PD) and 17 healthy controls. Behavioral events (hits and errors) constituted the driving input connected to the cerebellum, and the modulation in connectivity was assessed relative to the hit condition (successful interception of target)., Results: The driving input elicited response in the target area, while modulatory input changed the specific connection strength. The neuroimaging data revealed similar structure of intrinsic connectivity in both groups with unidirectional connections from cerebellum to both sides of the BG, from BG to the SMA, and then from SMA to the cerebellum. However, the type of intrinsic connection was different between two groups. In the PD group, the connection between the SMA and cerebellum was inhibitory in comparison to the HC group, where the connection was activated. Furthermore, the modulation of connectivity by the performance in the task was different between the two groups, with decreased connectivity between the cerebellum and left BG and SMA and a more pronounced symmetry of these connections in controls. In the same time, there was an increased EC between the cerebellum and both sides of BG with more pronounced asymmetry (stronger connection with left BG) in patients. In addition, in the PD group the modulatory input strengthened inhibitory connectivity between the SMA and the cerebellum, while in the HC group the excitatory connection was slightly strengthened., Conclusions: Our findings indicate that although early PD subjects and controls use similar functional circuits to maintain a successful outcome in predictive motor timing behavior, the type and strength of EC and its modulation by behavioral performance differ between these two groups. These functional differences might represent the first step of cortical reorganization aimed at maintaining a normal performance in the brain affected by early Parkinson's disease and may have implications for the neuro-rehabilitation field., (Copyright © 2013 by the American Society of Neuroimaging.)

Objectives: Abnormal task-related activation and connectivity is present in schizophrenia. The aim of this study was the analysis of functional networks in schizophrenia patients in remission after the first episode., Experimental Design: Twenty-nine male patients in remission after the first episode of schizophrenia and 22 healthy controls underwent examination by functional magnetic resonance during verbal fluency tasks (VFT). The functional connectivity of brain networks was analyzed using independent component analysis., Results: The patients showed lower activation of the salience network during VFT. They also showed lower deactivation of the default mode network (DMN) during VFT processing. Spectral analysis of the component time courses showed decreased power in slow frequencies of signal fluctuations in the salience and DMNs and increased power in higher frequencies in the left frontoparietal cortex reflecting higher fluctuations of the network activity. Moreover, there was decreased similarity of component time courses in schizophrenia—the patients had smaller negative correlation between VFT activated and deactivated networks, and smaller positive correlations between DMN subcomponents., Conclusions: There is still an abnormal functional connectivity of several brain networks in remission after the first episode of schizophrenia. The effect of different treatment modalities on brain connectivity, together with temporal dynamics of this functional abnormality should be the objective of further studies to assess its potential as a marker of disease stabilization.

Unlabelled: Schizophrenia is a condition with a highly variable course that is hard to predict. The aim of the present study was to investigate if local gray matter volume (GMV) can differentiate poor (PF) and good (GF) functioning patients using voxel-wise analysis in a group of first-episode schizophrenia subjects (FES)., Method: 32 FES male patients were assessed twice: at the time of the first episode of schizophrenia and one year later. 18 healthy controls matched for age, gender, and handedness were also included. Local gray matter volume was analyzed using voxel-wise full-factorial design with factors group (GF, PF) and time., Results: FES subjects had bilateral gray matter reduction in the lateral prefrontal cortex as compared with healthy controls. PF subjects had smaller GMV in the left orbitofrontal and frontopolar cortex., Conclusion: GMV in the left prefrontal cortex differentiates later poor and good functioning schizophrenia patients. Morphological analysis might be considered a candidate for a biological marker in outcome prediction. However, the small sample size, and the lack of female subjects limit generalization of results. Moreover, studies analyzing the predictive value of brain morphology on a single-subject level should be performed to assess its real usefulness in outcome prediction.

The analysis of functional magnetic resonance imaging (fMRI) data involves multiple stages of data pre-processing before the activation can be statistically detected. Spatial smoothing is a very common pre-processing step in the analysis of functional brain imaging data. This study presents a broad perspective on the influence of spatial smoothing on fMRI group activation results. The data obtained from 20 volunteers during a visual oddball task were used for this study. Spatial smoothing using an isotropic gaussian filter kernel with full width at half maximum (FWHM) sizes 2 to 30 mm with a step of 2 mm was applied in two levels - smoothing of fMRI data and/or smoothing of single-subject contrast files prior to general linear model random-effects group analysis generating statistical parametric maps. Five regions of interest were defined, and several parameters (coordinates of nearest local maxima, t value, corrected threshold, effect size, residual values, etc.) were evaluated to examine the effects of spatial smoothing. The optimal filter size for group analysis is discussed according to various criteria. For our experiment, the optimal FWHM is about 8 mm. We can conclude that for robust experiments and an adequate number of subjects in the study, the optimal FWHM for single-subject inference is similar to that for group inference (about 8 mm, according to spatial resolution). For less robust experiments and fewer subjects in the study, a higher FWHM would be optimal for group inference than for single-subject inferences.

Parkinson's disease (PD) affects speech, including respiration, phonation, and articulation. We measured the blood oxygen level-dependent (BOLD) response to overt sentence reading in: (1) 9 treated female patients with mild to moderate PD (age; mean 66.0 +/- 11.6 years, mean levodopa equivalent 583.3 +/- 397.9 mg) and (2) 8 age-matched healthy female controls (age; mean 62.2 years +/- 12.3). Speech was recorded in the scanner to assess which brain regions underlie variations in the initiation and paralinguistic aspects (e.g., pitch, loudness, and rate) of speech production in the two groups. There were no differences in paralinguistic aspects of speech except for speech loudness; it was lower in PD patients compared with that in controls, when age was used as a covariate. In both groups, we observed increases in the BOLD response (reading-baseline) in brain regions involved in speech production and perception. In PD patients, as compared with controls, we found significantly higher BOLD signal in the right primary orofacial sensorimotor cortex and more robust correlations between the measured speech parameters and the BOLD response to reading, particularly, in the left primary orofacial sensorimotor cortex. These results might reflect compensatory mechanisms and/or treatment effects that take place in mild to moderately ill PD patients with quality of speech yet comparable with that of age-matched controls., ((c) 2007 Movement Disorder Society.)

Introduction: Movement artifacts can cause serious changes in fMRI time series and therefore induce false positive or false negative results. In this work we calculated maps of movement artifacts using various approaches and compared maps across three different datasets to assess the variability between different populations and acquisition parameters. Methods: Three different data sets (each under different experimental task, TR and number of scans, each consisted of 50 subjects), were included into the evaluation. The data was processed using SPM8. 6 parameters obtained during realignment were extended by adding their first differences and their squares to get up to 24 parameters included in the design matrix. Subsequently these movement parameters were tested for their statistical significance on explanation of variability in the data. Probability maps were created by calculating relative frequency of occurrence of “movement-based activation” within each dataset. Power maps were calculated from the ratio of movement-based signal energy to the energy of the total measured signal. Results: In this work, we demonstrated that using all of 24 parameters will explain more variability in the data related to movement artifacts. We found out that probability maps show the similar location of artifacts across datasets nevertheless maps differ in frequency of occurrence. Power maps indicate similar places as probability maps but the amount of the movement-based variability is different across datasets. Conclusion: We can conclude, that movement artifacts influence the signal at similar areas within subjects and datasets. Movement artifacts are mainly localized at the edges of the brain and brain ventricles. However, consistency of artifact occurrence and power of movement-based signal is different between studies. [ABSTRACT FROM AUTHOR]