Local Signal Changes Research Articles

Laminar fMRI using T2-prepared multi-echo FLASH

Functional magnetic resonance imaging (fMRI) using blood oxygenation level dependent (BOLD) contrast at a sub-millimeter scale is a promising technique to probe neural activity at the level of cortical layers. While gradient echo (GRE) BOLD sequences exhibit the highest sensitivity, their signal is confounded by unspecific extravascular (EV) and intravascular (IV) effects of large intracortical ascending veins and pial veins leading to a downstream blurring effect of local signal changes. In contrast, spin echo (SE) fMRI promises higher specificity towards signal changes near the microvascular compartment. However, the T2-weighted signal is typically sampled with a gradient echo readout imposing additional T2’-weighting.In this work, we used a T2-prepared (T2-prep) sequence with short GRE readouts to investigate its capability to acquire laminar fMRI data during a visual task in humans at 7 T. By varying the T2-prep echo time (TEprep) and acquiring multiple gradient echoes (TEGRE) per excitation, we studied the specificity of the sequence and the influence of possible confounding contributions to the shape of laminar fMRI profiles. By fitting and extrapolating the multi-echo GRE data to a TEGRE = 0 ms condition, we show for the first time laminar profiles free of T2’-pollution, confined to gray matter. This finding is independent of TEprep, except for the shortest one (31 ms) where hints of a remaining intravascular component can be seen. For TEGRE > 0 ms a prominent peak at the pial surface is observed that increases with longer TEGRE and dominates the shape of the profiles independent of the amount of T2-weighting. Simulations show that the peak at the pial surface is a result of static EV dephasing around pial vessels in CSF visible in GM due to partial voluming. Additionally, another, weaker, static dephasing effect is observed throughout all layers of the cortex, which is particularly obvious in the data with shortest T2-prep echo time. Our simulations show that this cannot be explained by intravascular dephasing but that it is likely caused by extravascular effects of the intracortical and pial veins. We conclude that even for TEGRE as short as 2.3 ms, the T2’-weighting added to the T2-weighting is enough to dramatically affect the laminar specificity of the BOLD signal change. However, the bulk of this corruption stems from CSF partial volume effects which can in principle be addressed by increasing the spatial resolution of the acquisition.

Estimation of intraday stock market resiliency: Short-Time Fourier Transform approach

In this study, a new methodology for intraday stock resiliency measurement based on the Short-Time Fourier Transform (STFT) for high-frequency data is introduced and utilized. The STFT is a Discrete Fourier Transform (DFT) variant that measures local signal changes over time. Resiliency is one of stock market liquidity dimensions and, as an estimate of price dynamism, is not constant over a given time period. The findings of empirical experiments for real-data from the Warsaw Stock Exchange confirm that the new proxy enables us to unveil and investigate intraday patterns of resiliency. We wish to emphasize both theoretical and practical aspects of market resiliency measurement.

Using dynamic time warping for quantifying effects of sinusoidal oscillation deviations during EEG time series prediction and for finding interesting EEG and fMRI changes

Dynamic time warping (DTW) has successfully been used in feature extraction and analysis of brain data [1,2]. However, DTW and other dynamic programming (DP) methods are not nearly as widespread in neuroscientific contexts, as they are in bioinformatics, where they are central [3]. DP algorithms are extremely powerful and efficient [4], so we explored further uses of DTW in the analysis of brain data. The first part a) applies DTW to successfully find locally interesting signal changes in EEG and functional magnetic resonance imaging (fMRI) data. In the second part b) we focused on harmonic analysis. Harmonic analysis as conducted via Fourier Transform (FT) or Wavelet Transform (WT) is extremely widespread in analyzing brain data, especially electroencephalography (EEG) data. The fundamental assumption of these methods is that a complex function (the time series waveform) can be represented as a superposition of sinusoidal oscillations. While comparing spectra is useful, it is incorrect to assume that the entirety of the spectral density is due to oscillatory activity proper (especially sinusoidal) [5]. We wanted to get some idea of the amount and effects of non-sinusoidal oscillations in EEG data. In a) we quantify the amount of local signal dissimilarity, which is often equivalent to where there is interesting sudden activity, and in b) we quantify how much non-sinusoidal activity there is in spectra computed in a standard way. For a) we apply the method on a data set from a simultaneous EEG/fMRI session with 18 subjects that has been used in other published works before [6] and show that we can easily detect movement, changes in brain state accompanying large reaction time changes during a choice reaction time task, and other interesting events in the EEG and fMRI data sets. The method is applicable to both modalities, as DTW is very general and can be used with any type of time series. The method we used works by either running DTW on a selected time interval (or volume) against sequential intervals (or volumes), or by running it as a sliding-window for detecting sudden local signal changes in the EEG or fMRI. For b) we show that the spectral power, as it's typically computed, includes non-sinusoidal activity, when compared to the signal compared against a series of equal-length (to the signal) sinusoidal waves of varying frequencies. We also found when the sinusoidal-oscillation deviations occurred during an EEG time series extrapolation which we performed using a deep learning Toolbox. The EEG time series prediction gave another point of view for the oscillation changes. We find that the prediction breakdown points are related to sudden oscillation changes, which are presumably time points when other neural oscillator generators become active and mixed in the signal. We interpret both prediction breakdowns and momentary oscillatory deviations as being possible reflections of sudden phase changes in the local or global brain state related, which may be related to EEG microstate changes. We hope to bring to attention the flexibility and power of DP to the neuroscientific community.

Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions

Planetary gearbox fault diagnosis under nonstationary conditions is important for many engineering applications in general and for wind turbines in particular because of their time-varying operating conditions. This paper focuses on the identification of time-varying characteristic frequencies from complex nonstationary vibration signals for fault diagnosis of wind turbines under nonstationary conditions. We propose a time–frequency analysis method based on the Vold-Kalman filter and higher order energy separation (HOES) to extract fault symptoms. The Vold-Kalman filter is improved such that it is encoders/tachometers-free. It can decompose an arbitrarily complex signal into mono-components without resorting to speed inputs, thus satisfying the mono-component requirement by the HOES algorithm. The HOES is then used to accurately estimate the instantaneous frequency because of its high adaptability to local signal changes. The derived time–frequency distribution features fine resolution without cross-term interferences and thus facilitates extracting time-varying frequency components from highly complex and nonstationary signals. The method is illustrated and validated by analyzing simulated and experimental signals of a planetary gearbox in a wind turbine test rig under nonstationary running conditions. The results have shown that the method is effective in detecting both distributed (wear on every tooth) and localized (chipping on one tooth) gear faults.

Effect of hypoglycemia on brain structure in people with type 2 diabetes: epidemiological analysis of the ACCORD-MIND MRI trial.

OBJECTIVEThe effect of hypoglycemia related to treatment of type 2 diabetes mellitus (T2DM) on brain structure remains unclear. We aimed to assess whether symptomatic severe hypoglycemia is associated with brain atrophy and/or white matter abnormalities.RESEARCH DESIGN AND METHODSWe included T2DM participants with brain MRI from the Action to Control Cardiovascular Risk in Diabetes-Memory in Diabetes (ACCORD-MIND) trial. Symptomatic severe hypoglycemia was defined as blood glucose <2.8 mmol/L or symptoms resolved with treatments that required the assistance of another person or medical assistance (hypoglycemia requiring assistance [HA]). Standardized brain MRI was performed at baseline and at 40 months. Total brain volume (TBV) and abnormal white matter (AWM) volume were calculated using an automated computer algorithm. Brain MRI scans of hypoglycemic participants were also reviewed for local disease.RESULTSOf the 503 T2DM participants (mean age, 62 years) with successful baseline and 40-month brain MRI, 28 had at least one HA episode during the 40-month follow-up. Compared with participants without HA, those with HA had marginally significant less atrophy (less decrease in TBV) from baseline to 40 months (−9.55 [95% CI −15.21, −3.90] vs. −15.38 [95% CI −16.64, −14.12], P = 0.051), and no significant increase of AWM volume (2.06 [95% CI 1.71, 2.49] vs. 1.84 [95% CI 1.76, 1.91], P = 0.247). In addition, no unexpected local signal changes or volume loss were seen on hypoglycemic participants’ brain MRI scans.CONCLUSIONSOur study suggests that hypoglycemia related to T2DM treatment may not accentuate brain pathology, specifically brain atrophy or white matter abnormalities.

The Pathophysiology of Primary Hip Osteoarthritis may Originate from Bone Alterations

Objectives: The aim of this study was to investigate whether bone alterations detected by hip magnetic resonance imaging (MRI) were associated with subsequent primary hip OA. Methods: We enrolled 7 patients with hip joint pain from their first visit, at which hip joints were classified as grade 0 or I on the Kellgren-Lawrence grading scale. Plain radiographs and magnetic resonance imaging (MRI) were performed on all cases, and pain was assessed with the Denis pain scale. Average age, height, weight, body mass index, bone mineral density (L1-4), central edge angle, Sharp’s angle, and acetabular hip index were calculated. Results: Within two months of the onset of pain, 4 of the 7 cases showed broad bone signal changes, while 3 cases showed local signal changes in the proximal femur on hip MRI. Three to 6 months after the onset of pain, in all patients whose pain was much improved, plain radiographs showed progression to further-stage OA. Conclusion: Our findings suggest that bone abnormalities in the proximal femur might be involved in the pathogenesis of primary hip OA.

Effects of simultaneous EEG recording on MRI data quality at 1.5, 3 and 7 tesla

Although the focus of attention on data degradation during simultaneous MRI/EEG recording has to date largely been upon EEG artefacts, the presence of the conducting wires and electrodes of the EEG recording system also causes some degradation of MRI data quality. This may result from magnetic susceptibility effects which lead to signal drop-out and image distortion, as well as the perturbation of the radiofrequency fields, which can cause local signal changes and a global reduction in the signal to noise ratio (SNR) of magnetic resonance images. Here, we quantify the effect of commercially available 32 and 64 electrode caps on the quality of MR images obtained in scanners operating at magnetic fields of 1.5, 3 and 7 T, via the use of MR-based, field-mapping techniques and analysis of the SNR in echo planar image time series. The electrodes are shown to be the dominant source of magnetic field inhomogeneity, although the localised nature of the field perturbation that they produce means that the effect on the signal intensity from the brain is not significant. In the particular EEG caps investigated here, RF inhomogeneity linked to the longer ECG and EOG leads causes some reduction in the signal intensity in images obtained at 3 and 7 T. Measurements of the standard deviation of white matter signal in EPI time series indicates that the introduction of the EEG cap produces a small reduction in the image signal to noise ratio, which increases with the number of electrodes used.

IRM fonctionnelle cérébrale : bases physiologiques, techniques et applications cliniques

Brain functional MRI (fMRI) provides an indirect mapping of cerebral activity, based on the detection of local changes in blood flow and oxygenation levels that are associated with neuronal activity (BOLD contrast). fMRI allows noninvasive studies of normal and pathological aspects of the brain's functional organization. It is based on the comparison of two or more cognitive states. Echoplanar imaging is the technique of choice, providing the quickest study of the entire brain. Activation maps are calculated from a statistical analysis of the local signal changes. fMRI has become one of the most widely used functional imaging techniques in neuroscience. In clinical practice, fMRI can identify eloquent areas involved in motor and language functions in surgical patients and can evaluate the risk of postoperative neurological deficit.

Multifocal fMRI mapping of visual cortical areas

The multifocal mapping of electroretinograms and visual evoked potentials has established an important role in both basic research and in diagnostic procedures. We have developed a multifocal mapping method for fMRI, which allows detailed analysis of multiple local visual field representations in the cortex with excellent spatial resolution. Visual field was divided into 60 regions in a dartboard configuration, scaled according to the human magnification factor. Within blocks of 7 s, half of the regions were stimulated with checkerboard patterns contrast reversing at 8 reversals per second, while the other half remained inactive at uniform luminance. The subset of active regions changed with each 7-s block, according to an orthogonal design. Functional MRI was done with a 3-T GE Signa and analyzed with SPM2. A general linear model was fitted producing activation maps for each of the 60 regions, and local signal changes were quantified from V1. These activation maps were next assigned to 3D surface models of the cortical sheet, and then unfolded, using the Brain à la Carte software package. Phase-encoded retinotopic analysis of conventional design served as qualitative comparison data. With multifocal fMRI, all regions were mapped with good signal-to-noise ratio in V1, and subsets of regions showed activation in V2 and V3. This method allows rapid and direct exploration of multiple local visual responses, and is thus able to give complementary information to phase encoded mapping of retinotopic areas.

A dynamic system model-based technique for functional MRI data analysis

Signals in functional magnetic resonance imaging (fMRI) are influenced by physiological fluctuations in addition to local brain activity. We have proposed a dynamic system model-based technique for separation of signal changes related to brain activation inputs from those related to physiological fluctuations. We applied this technique to a visual fMRI experiment to determine the validity and feasibility of this technique for fMRI data analyses. Gradient-echo echo planar images were obtained from 12 healthy volunteers with a Siemens ALLEGRA operating at 3 T, with a repetition time of 500 ms, echo time of 20 ms, field of view of 200–210 mm, matrix size of 64 × 64, and slice thickness of 5 mm. Twelve runs with two stimulation periods of varied duration (2–8 s) with 8-Hz flickering illumination were obtained for each subject. Local signal changes were modeled by an autoregressive model with two exogenous inputs, a visual stimulation input and a global reference signal. Local signal changes were appropriately predicted not only for stimulation periods but also resting periods. A significant linear relationship was found between model static gain based on the dynamic system modeling and beta coefficient based on a general linear model (GLM) analysis for active voxels in the primary visual cortex (analysis of covariance [ANCOVA], P < 0.001; estimated parameter, 0.967; 95% confidence interval, 0.734–1.201). This dynamic system model-based technique is sufficiently accurate and feasible for use in extracting signal changes related to brain activation inputs from measured signals with physiological fluctuations.

Quantification of signal changes in gradient recalled echo FMRI

Understanding and quantifying the various contributions to functional magnetic resonance imaging (FMRI) signal changes in activated cortical areas is paramount for a clinical application of brain mapping by FMRI. Therefore, all significant contributions to FMRI signal changes, both extra- and intravascular, from macrovessels down to the capillary network, should be taken into account. We present a gradient-recalled-echo FMRI model based on in-flow effects described by the Bloch equations, adding susceptibility effects empirically via T2∗ differences measured in vitro in human blood samples. Results of these calculations (by systematically varying α, echo time (TE), repetition time (TR), as well as blood velocity and T2∗ upon stimulation) may be used to (a) simulate functional MRI experiments with different measurement protocols and (b) estimate realistic values for important anatomical and physiological details that influence local signal changes in FMRI (i.e., size and distribution of vessels, effective relaxation times of blood, etc.). The excellent agreement between our model calculations and experimental results from conventional gradient recalled echo fMRI in vivo suggests a significant contribution from very slow flow and oxygenation changes, predominantly in small vessels (vblood = 1–4 mm/s). The actual contribution of T1- and T2-related effects is strongly dependent on sequence design and actual sequence parameters used. Thus, the model simulations presented may also be used to optimize measurement protocols for investigating various neurophysiological phenomena.