Improved cerebrovascular reactivity mapping using coherence weighted general linear model in the frequency domain

Cerebrovascular reactivity (CVR) is a prognostic indicator of cerebrovascular health. Estimating CVR from endogenous end-tidal carbon dioxide (CO2) fluctuation and MRI signal recorded under resting state can be difficult due to the poor signal-to-noise ratio (SNR) of signals. Thus, we aimed to improve the method of estimating CVR from end-tidal CO2 and MRI signals. We proposed a coherence weighted general linear model (CW-GLM) to estimate CVR from the Fourier coefficients weighted by the signal coherence in frequency domain, which confers two advantages. First, it requires no signal alignment in time domain, which simplifies experimental methods. Second, it limits the GLM analysis within the frequency band where CO2 and MRI signals are highly correlated, which automatically suppresses noise and nuisance signals. We compared the performance of our method with time-domain GLM (TD-GLM) and frequency-domain GLM (FD-GLM) in both synthetic and in-vivo data; wherein we calculated CVR from signals recorded under both resting state and sinusoidal stimulus. In synthetic data, CW-GLM has a remarkable performance on CVR estimation from narrow band signals with a mean-absolute error of 0.7 % (gray matter) and 1.2 % (white matter), which was lower than all the other methods. Meanwhile, CW-GLM maintains a comparable performance on CVR estimation from resting signals, with a mean-absolute error of 4.1 % (gray matter) and 8 % (white matter). The superior performance was maintained across the 36 in-vivo measurements, with CW-GLM exhibiting limits of agreement of -16.7 % - 9.5 % between CVR calculated from the resting and sinusoidal CO2 paradigms which was 12 % - 209 % better than current time-domain methods. Evaluating of the cross-coherence spectrum revealed highest signal coherence within the frequency band from 0.01 Hz to 0.05 Hz, which overlaps with previously recommended frequency band (0.02 Hz to 0.04 Hz) for CVR analysis. Our data demonstrates that CW-GLM can work as a self-adaptive band-pass filter to improve CVR robustness, while also avoiding the need for signal temporal alignment.

Sinusoidal CO2 respiratory challenge for concurrent perfusion and cerebrovascular reactivity MRI

Introduction: Deoxygenation-based dynamic susceptibility contrast (dDSC) has previously leveraged respiratory challenges to modulate blood oxygen content as an endogenous source of contrast alternative to gadolinium injection in perfusion-weighted MRI. This work proposed the use of sinusoidal modulation of end-tidal CO2 pressures (SineCO 2 ), which has previously been used to measure cerebrovascular reactivity, to induce susceptibility-weighted gradient-echo signal loss to measure brain perfusion. Methods: SineCO 2 was performed in 10 healthy volunteers (age 37 ± 11, 60% female), and tracer kinetics model was applied in the frequency domain to calculate cerebral blood flow, cerebral blood volume, mean transit time, and temporal delay. These perfusion estimates were compared against reference techniques, including gadolinium-based DSC, arterial spin labeling, and phase contrast. Results: Our results showed regional agreement between SineCO 2 and the clinical comparators. SineCO 2 was able to generate robust CVR maps in conjunction to baseline perfusion estimates. Discussion: Overall, this work demonstrated feasibility of using sinusoidal CO2 respiratory paradigm to simultaneously acquire both cerebral perfusion and cerebrovascular reactivity maps in one imaging sequence.

Chronic anemia: The effects on the connectivity of white matter

Chronic anemia is commonly observed in patients with hemoglobinopathies, mainly represented by disorders of altered hemoglobin (Hb) structure (sickle cell disease, SCD) and impaired Hb synthesis (e.g. thalassemia syndromes, non-SCD anemia). Both hemoglobinopathies have been associated with white matter (WM) alterations. Novel structural MRI research in our laboratory demonstrated that WM volume was diffusely lower in deep, watershed areas proportional to anemia severity. Furthermore, diffusion tensor imaging analysis has provided evidence that WM microstructure is disrupted proportionally to Hb level and oxygen saturation. SCD patients have been widely studied and demonstrate lower fractional anisotropy (FA) in the corticospinal tract and cerebellum across the internal capsule and corpus callosum. In the present study, we compared 19 SCD and 15 non-SCD anemia patients with a wide range of Hb values allowing the characterization of the effects of chronic anemia in isolation of sickle Hb. We performed a tensor analysis to quantify FA changes in WM connectivity in chronic anemic patients. We calculated the volumetric mean of FA along the pathway of tracks connecting two regions of interest defined by BrainSuite's BCI-DNI atlas. In general, we found lower FA values in anemic patients; indicating the loss of coherence in the main diffusion direction that potentially indicates WM injury. We saw a positive correlation between FA and hemoglobin in these same regions, suggesting that decreased WM microstructural integrity FA is highly driven by chronic hypoxia. The only connection that did not follow this pattern was the connectivity within the left middle-inferior temporal gyrus. Interestingly, more reductions in FA were observed in non-SCD patients (mainly along with intrahemispheric WM bundles and watershed areas) than the SCD patients (mainly interhemispheric).

Anemia Increases Oxygen Extraction Fraction in Deep Brain Structures but Not in the Cerebral Cortex

Sickle cell disease (SCD) is caused by a single amino acid mutation in hemoglobin, causing chronic anemia and neurovascular complications. However, the effects of chronic anemia on oxygen extraction fraction (OEF), especially in deep brain structures, are less well understood. Conflicting OEF values have been reported in SCD patients, but have largely attributed to different measurement techniques, faulty calibration, and different locations of measurement. Thus, in this study, we investigated the reliability and agreement of two susceptibility-based methods, quantitative susceptibility mapping (QSM) and complex image summation around a spherical or a cylindrical object (CISSCO), for OEF measurements in internal cerebral vein (ICV), reflecting oxygen saturation in deep brain structures. Both methods revealed that SCD patients and non-sickle anemia patients (ACTL) have increased OEF in ICV (42.6% ± 5.6% and 30.5% ± 3.6% in SCD by CISSCO and QSM respectively, 37.0% ± 4.1% and 28.5% ± 2.3% in ACTL) compared with controls (33.0% ± 2.3% and 26.8% ± 1.8%). OEF in ICV varied reciprocally with hematocrit (r ² = 0.92, 0.53) and oxygen content (r ² = 0.86, 0.53) respectively. However, an opposite relationship was observed for OEF measurements in sagittal sinus (SS) with the widely used T₂-based oximetry, T₂-Relaxation-Under-Spin-Tagging (TRUST), in the same cohorts (31.2% ± 6.6% in SCD, 33.3% ± 5.9% in ACTL and 36.8% ± 5.6% in CTL). Importantly, we demonstrated that hemoglobin F and other fast moving hemoglobins decreased OEF by TRUST and explained group differences in sagittal sinus OEF between anemic and control subjects. These data demonstrate that anemia causes deep brain hypoxia in anemia subjects with concomitant preservation of cortical oxygenation, as well as the key interaction of the hemoglobin dissociation curve and cortical oxygen extraction.

Imputation Strategy for Reliable Regional MRI Morphological Measurements

Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this inherent variability, the errors are often identified through visual inspection and then corrected (semi)manually. Identification and correction of incorrect segmentation could be very expensive for large-scale studies. While identification of the incorrect results can be done relatively fast even with manual inspection, the correction step is extremely time-consuming, as it requires training staff to perform laborious manual corrections. Here we frame the correction phase of this problem as a missing data problem. Instead of manually adjusting the segmentation outputs, our computational approach aims to derive accurate morphological measures by machine learning imputation. Data imputation techniques may be used to replace missing or incorrect region average values with carefully chosen imputed values, all of which are computed based on other available multivariate information. We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. A random forest imputation technique recovered the gold standard results with a significant accuracy (r = 0.93, p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). The random forest technique proved to be most effective for big data studies (N > 250).