Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well ...as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.
•Numerous techniques are available for denoising the BOLD fMRI signal.•Motion-related artifacts and physiological noise fluctuations are the main targets.•Phase-based and multi-echo fMRI can help to improve the performance of denoising.•There exist multiple equally-efficient alternatives to global signal regression.•There is no “best” method for preprocessing, but there are incorrect methods.
Parkinson's disease is a common neurodegenerative disorder in which gastrointestinal symptoms may appear prior to motor symptoms. The gut microbiota of patients with Parkinson's disease shows unique ...changes, which may be used as early biomarkers of disease. Alterations in the gut microbiota composition may be related to the cause or effect of motor or non-motor symptoms, but the specific pathogenic mechanisms are unclear. The gut microbiota and its metabolites have been suggested to be involved in the pathogenesis of Parkinson's disease by regulating neuroinflammation, barrier function and neurotransmitter activity. There is bidirectional communication between the enteric nervous system and the CNS, and the microbiota-gut-brain axis may provide a pathway for the transmission of α-synuclein. We highlight recent discoveries about alterations to the gut microbiota in Parkinson's disease and focus on current mechanistic insights into the microbiota-gut-brain axis in disease pathophysiology. Moreover, we discuss the interactions between the production and transmission of α-synuclein and gut inflammation and neuroinflammation. In addition, we draw attention to diet modification, the use of probiotics and prebiotics and faecal microbiota transplantation as potential therapeutic approaches that may lead to a new treatment paradigm for Parkinson's disease.
Observations of sea surface and land–near-surface merged temperature anomalies are used to monitor climate variations and to evaluate climate simulations; therefore, it is important to make analyses ...of these data as accurate as possible. Analysis uncertainty occurs because of data errors and incomplete sampling over the historical period. This manuscript documents recent improvements in NOAA’s merged global surface temperature anomaly analysis, monthly, in spatial 5° grid boxes. These improvements allow better analysis of temperatures throughout the record, with the greatest improvements in the late nineteenth century and since 1985. Improvements in the late nineteenth century are due to improved tuning of the analysis methods. Beginning in 1985, improvements are due to the inclusion of bias-adjusted satellite data. The old analysis (version 2) was documented in 2005, and this improved analysis is called version 3.
fMRI clustering and false-positive rates Cox, Robert W.; Chen, Gang; Glen, Daniel R. ...
Proceedings of the National Academy of Sciences - PNAS,
04/2017, Letnik:
114, Številka:
17
Journal Article
Recent reports of inflated false-positive rates (FPRs) in FMRI group analysis tools by Eklund and associates in 2016 have become a large topic within (and outside) neuroimaging. They concluded that ...existing parametric methods for determining statistically significant clusters had greatly inflated FPRs ("up to 70%," mainly due to the faulty assumption that the noise spatial autocorrelation function is Gaussian shaped and stationary), calling into question potentially "countless" previous results; in contrast, nonparametric methods, such as their approach, accurately reflected nominal 5% FPRs. They also stated that AFNI showed "particularly high" FPRs compared to other software, largely due to a bug in 3dClustSim. We comment on these points using their own results and figures and by repeating some of their simulations. Briefly, while parametric methods show some FPR inflation in those tests (and assumptions of Gaussian-shaped spatial smoothness also appear to be generally incorrect), their emphasis on reporting the single worst result from thousands of simulation cases greatly exaggerated the scale of the problem. Importantly, FPR statistics depends on "task" paradigm and voxelwise p value threshold; as such, we show how results of their study provide useful suggestions for FMRI study design and analysis, rather than simply a catastrophic downgrading of the field's earlier results. Regarding AFNI (which we maintain), 3dClustSim's bug effect was greatly overstated-their own results show that AFNI results were not "particularly" worse than others. We describe further updates in AFNI for characterizing spatial smoothness more appropriately (greatly reducing FPRs, although some remain >5%); in addition, we outline two newly implemented permutation/randomization-based approaches producing FPRs clustered much more tightly about 5% for voxelwise p ≤ 0.01.
SUMA Saad, Ziad S.; Reynolds, Richard C.
NeuroImage,
08/2012, Letnik:
62, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Surface-based brain imaging analysis offers the advantages of preserving the topology of cortical activation, increasing statistical power of group-level statistics, estimating cortical thickness, ...and visualizing with ease the pattern of activation across the whole cortex. SUMA is an open-source suite of programs for performing surface-based analysis and visualization. It was designed since its inception to allow for a fine control over the mapping between volume and surface domains, and for very fast and simultaneous display of multiple surface models and corresponding multitudes of datasets, all while maintaining a direct two-way link to volumetric data from which surface models and data originated. SUMA provides tools for performing spatial operations such as controlled smoothing, clustering, and interactive ROI drawing on folded surfaces in 3D, in addition to the various level-1 and level-2 FMRI statistics including FDR and FWE correction for multiple comparisons. In our contribution to this commemorative issue of Neuroimage we touch on the importance of surface-based analysis and provide a historic backdrop that motivated the creation of SUMA. We also highlight features that are particular to SUMA, notably the standardization procedure of meshes to greatly facilitate group-level analyses, and the ability to control SUMA's graphical interface from external programs making it possible to handle large collections of data with relative ease.
Progressive forms of multiple sclerosis (MS) are associated with chronic demyelination, axonal loss, neurodegeneration, cortical and deep gray matter damage, and atrophy. These changes are strictly ...associated with compartmentalized sustained inflammation within the brain parenchyma, the leptomeninges, and the cerebrospinal fluid. In progressive MS, molecular mechanisms underlying active demyelination differ from processes that drive neurodegeneration at cortical and subcortical locations. The widespread pattern of neurodegeneration is consistent with mechanisms associated with the inflammatory molecular load of the cerebrospinal fluid. This is at variance with gray matter demyelination that typically occurs at focal subpial sites, in the proximity of ectopic meningeal lymphoid follicles. Accordingly, it is possible that variations in the extent and location of neurodegeneration may be accounted for by individual differences in CSF flow, and by the composition of soluble inflammatory factors and their clearance. In addition, "double hit" damage may occur at sites allowing a bidirectional exchange between interstitial fluid and CSF, such as the Virchow-Robin spaces and the periventricular ependymal barrier. An important aspect of CSF inflammation and deep gray matter damage in MS involves dysfunction of the blood-cerebrospinal fluid barrier and inflammation in the choroid plexus. Here, we provide a comprehensive review on the role of intrathecal inflammation compartmentalized to CNS and non-neural tissues in progressive MS.
The pathogenetic mechanisms underlying neuronal death and dysfunction in Alzheimer's disease (AD) remain unclear. However, chronic neuroinflammation has been implicated in stimulating or exacerbating ...neuronal damage. The tumor necrosis factor (TNF) superfamily of cytokines are involved in many systemic chronic inflammatory and degenerative conditions and are amongst the key mediators of neuroinflammation. TNF binds to the TNFR1 and TNFR2 receptors to activate diverse cellular responses that can be either neuroprotective or neurodegenerative. In particular, TNF can induce programmed necrosis or necroptosis in an inflammatory environment. Although activation of necroptosis has recently been demonstrated in the AD brain, its significance in AD neuron loss and the role of TNF signaling is unclear. We demonstrate an increase in expression of multiple proteins in the TNF/TNF receptor-1-mediated necroptosis pathway in the AD post-mortem brain, as indicated by the phosphorylation of RIPK3 and MLKL, predominantly observed in the CA1 pyramidal neurons. The density of phosphoRIPK3 + and phosphoMLKL + neurons correlated inversely with total neuron density and showed significant sexual dimorphism within the AD cohort. In addition, apoptotic signaling was not significantly activated in the AD brain compared to the control brain. Exposure of human iPSC-derived glutamatergic neurons to TNF increased necroptotic cell death when apoptosis was inhibited, which was significantly reversed by small molecule inhibitors of RIPK1, RIPK3, and MLKL. In the post-mortem AD brain and in human iPSC neurons, in response to TNF, we show evidence of altered expression of proteins of the ESCRT III complex, which has been recently suggested as an antagonist of necroptosis and a possible mechanism by which cells can survive after necroptosis has been triggered. Taken together, our results suggest that neuronal loss in AD is due to TNF-mediated necroptosis rather than apoptosis, which is amenable to therapeutic intervention at several points in the signaling pathway.
Objective
Prominent inflammation with formation of ectopic B‐cell follicle‐like structures in the meninges in secondary progressive multiple sclerosis (MS) (SPMS) is associated with extensive ...cortical pathology and an exacerbated disease course. Our objective was to evaluate the cellular substrates of the cortical damage to understand the role of meningeal inflammation in MS pathology.
Methods
Using >600 tissue blocks from 37 cases of SPMS and 14 non‐neurological controls, we carried out a detailed quantitative analysis of cortical atrophy and layer‐specific changes in cell populations in SPMS cases with (F+ SPMS) and without (F− SPMS) B‐cell follicle‐like structures.
Results
B‐cell follicle‐like structures were detected in the inflamed meninges of 20 of 37 SPMS cases (54%) and were associated with increased subpial cortical demyelination and cortical atrophy. A clear gradient of neuronal loss was observed in grey matter lesions and normal‐appearing grey matter in the motor cortex of F+ SPMS cases. The density of pyramidal neurons was significantly reduced in layers III and V of the motor cortex. Neuronal loss was accompanied by glia limitans damage with astrocyte loss and an opposite gradient of increased density of activated microglia. No gradient of neuronal loss was seen in F− SPMS cases.
Interpretation
We demonstrate substantial cortical neurodegeneration and generalized cell loss in progressive MS in association with meningeal inflammation and lymphoid tissue formation, supporting the hypothesis that cytotoxic factors diffusing from the meningeal compartment contribute to grey matter pathology and the consequent increase in clinical disability. Ann Neurol 2010;68:477–493
LayNii: A software suite for layer-fMRI Huber, Laurentius (Renzo); Poser, Benedikt A.; Bandettini, Peter A. ...
NeuroImage (Orlando, Fla.),
08/2021, Letnik:
237
Journal Article
Recenzirano
Odprti dostop
•A new software toolbox is introduced for layer-specific functional MRI: LayNii.•LayNii is a suite of command-line executable C++ programs for Linux, Windows, and macOS.•LayNii is designed for ...layer-fMRI data that suffer from SNR and coverage constraints.•LayNii performs layerification in the native voxel space of functional data.•LayNii performs layer-smoothing, GE-BOLD deveining, QA, and VASO analysis.
High-resolution fMRI in the sub-millimeter regime allows researchers to resolve brain activity across cortical layers and columns non-invasively. While these high-resolution data make it possible to address novel questions of directional information flow within and across brain circuits, the corresponding data analyses are challenged by MRI artifacts, including image blurring, image distortions, low SNR, and restricted coverage. These challenges often result in insufficient spatial accuracy of conventional analysis pipelines. Here we introduce a new software suite that is specifically designed for layer-specific functional MRI: LayNii. This toolbox is a collection of command-line executable programs written in C/C++ and is distributed opensource and as pre-compiled binaries for Linux, Windows, and macOS. LayNii is designed for layer-fMRI data that suffer from SNR and coverage constraints and thus cannot be straightforwardly analyzed in alternative software packages. Some of the most popular programs of LayNii contain ‘layerification’ and columnarization in the native voxel space of functional data as well as many other layer-fMRI specific analysis tasks: layer-specific smoothing, model-based vein mitigation of GE-BOLD data, quality assessment of artifact dominated sub-millimeter fMRI, as well as analyses of VASO data.
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