T1-MPRAGE and T2-FLAIR segmentation of cortical and subcortical brain regions-an MRI evaluation study

The spine-brain axis: is spinal anatomy associated with brain volume?

First small sample studies indicate that disturbances of spinal morphology may impair craniospinal flow of cerebrospinal fluid and result in neurodegeneration. The aim of this study was to evaluate the association of cervical spinal canal width and scoliosis with grey matter, white matter, ventricular and white matter hyperintensity volumes of the brain in a large study sample. Four hundred participants underwent whole-body 3 T magnetic resonance imaging. Grey matter, white matter and ventricular volumes were quantified using a warp-based automated brain volumetric approach. Spinal canal diameters were measured manually at the cervical vertebrae 2/3 level. Scoliosis was evaluated using manual measurements of the Cobb angle. Linear binomial regression analyses of measures of brain volumes and spine anatomy were performed while adjusting for age, sex, hypertension, cholesterol levels, body mass index, smoking and alcohol consumption. Three hundred eighty-three participants were included [57% male; age: 56.3 (±9.2) years]. After adjustment, smaller spinal canal width at the cervical vertebrae 2/3 level was associated with lower grey matter (P = 0.034), lower white matter (P = 0.012) and higher ventricular (P = 0.006, inverse association) volume. Participants with scoliosis had lower grey matter (P = 0.005), lower white matter (P = 0.011) and larger brain ventricular (P = 0.003) volumes than participants without scoliosis. However, these associations were attenuated after adjustment. Spinal canal width at the cervical vertebrae 2/3 level and scoliosis were not associated with white matter hyperintensity volume before and after adjustment (P > 0.864). In our study, cohort smaller spinal canal width at the cervical vertebrae 2/3 level and scoliosis were associated with lower grey and white matter volumes and larger ventricle size. These characteristics of the spine might constitute independent risk factors for neurodegeneration.

Characterizing cognitive subtypes in schizophrenia using cortical curvature

Cognitive deficits are a core symptom of schizophrenia, but research on their neural underpinnings has been challenged by the heterogeneity in deficits' severity among patients. Here, we address this issue by combining logistic regression and random forest to classify two neuropsychological profiles of patients with high (HighCog) and low (LowCog) cognitive performance in two independent samples. We based our analysis on the cortical features grey matter volume (VOL), cortical thickness (CT), and mean curvature (MC) of N = 57 patients (discovery sample) and validated the classification in an independent sample (N = 52). We investigated which cortical feature would yield the best classification results and expected that the 10 most important features would include frontal and temporal brain regions. The model based on MC had the best performance with area under the curve (AUC) values of 76% and 73%, and identified fronto-temporal and occipital brain regions as the most important features for the classification. Moreover, subsequent comparison analyses could reveal significant differences in MC of single brain regions between the two cognitive profiles. The present study suggests MC as a promising neuroanatomical parameter for characterizing schizophrenia cognitive subtypes.

Cognitive and functional deficits are associated with white matter abnormalities in two independent cohorts of patients with schizophrenia

BACKGROUND

Significant evidence links white matter (WM) microstructural abnormalities to cognitive impairment in schizophrenia (SZ), but the relationship of these abnormalities with functional outcome remains unclear.

METHODS

In two independent cohorts (C1, C2), patients with SZ were divided into two subgroups: patients with higher cognitive performance (SZ-HCP-C1, n = 25; SZ-HCP-C2, n = 24) and patients with lower cognitive performance (SZ-LCP-C1, n = 25; SZ-LCP-C2, n = 24). Healthy controls (HC) were included in both cohorts (HC-C1, n = 52; HC-C2, n = 27). We compared fractional anisotropy (FA) of the whole-brain WM skeleton between the three groups (SZ-LCP, SZ-HCP, HC) by a whole-brain exploratory approach and an atlas-defined WM regions-of-interest approach via tract-based spatial statistics. In addition, we explored whether FA values were associated with Global Assessment of Functioning (GAF) scores in the SZ groups.

RESULTS

In both cohorts, mean FA values of whole-brain WM skeleton were significantly lower in the SCZ-LCP group than in the SCZ-HCP group. Whereas in C1 the FA of the frontal part of the left inferior fronto-occipital fasciculus (IFOF) was positively correlated with GAF score, in C2 the FA of the temporal part of the left IFOF was positively correlated with GAF score.

CONCLUSIONS

We provide robust evidence for WM microstructural abnormalities in SZ. These abnormalities are more prominent in patients with low cognitive performance and are associated with the level of functioning.

3-Dimensional Fluid and White Matter Suppression Magnetic Resonance Imaging Sequence Accelerated With Compressed Sensing Improves Multiple Sclerosis Cervical Spinal Cord Lesion Detection Compared With Standard 2-Dimensional Imaging

OBJECTIVES

Fluid and white matter suppression (FLAWS) is a recently proposed magnetic resonance sequence derived from magnetization-prepared 2 rapid acquisition gradient-echo providing 2 coregistered datasets with white matter- and cerebrospinal fluid-suppressed signal, enabling synthetic imaging with amplified contrast. Although these features are high potential for brain multiple sclerosis (MS) imaging, spinal cord has never been evaluated with this sequence to date. The objective of this work was therefore to assess diagnostic performance and self-confidence provided by compressed-sensing (CS) 3-dimensional (3D) FLAWS for cervical MS lesion detection on a head scan that includes the cervical cord without changing standard procedures.

MATERIALS AND METHODS

Prospective 3 T scans (MS first diagnosis or follow-up) acquired between 2019 and 2020 were retrospectively analyzed. All patients underwent 3D CS-FLAWS (duration: 5 minutes 40 seconds), axial T2 turbo spin echo covering cervical spine from cervicomedullary junction to the same inferior level as FLAWS, and sagittal cervical T2/short tau inversion recovery imaging. Two readers performed a 2-stage double-blind reading, followed by consensus reading. Wilcoxon tests were used to compare the number of detected spinal cord lesions and the reader's diagnostic self-confidence when using FLAWS versus the reference 2D T2-weighted imaging.

RESULTS

Fifty-eight patients were included (mean age, 40 ± 13 years, 46 women, 7 ± 6 years mean disease duration). The CS-FLAWS detected significantly more lesions than the reference T2-weighted imaging (197 vs 152 detected lesions, P < 0.001), with a sensitivity of 98% (T2-weighted imaging sensitivity: 90%) after consensual reading. Considering the subgroup of patients who underwent sagittal T2 + short tau inversion recovery imaging (Magnetic Resonance Imaging for Multiple Sclerosis subgroup), +250% lesions were detected with FLAWS (63 vs 25 lesions detected, P < 0.001). Mean reading self-confidence was significantly better with CS-FLAWS (median, 5 [interquartile range, 1] [no doubt for diagnosis] vs 4 [interquartile range, 1] [high confidence]; P < 0.001).

CONCLUSIONS

Imaging with CS-FLAWS provides an improved cervical spinal cord exploration for MS with increased self-confidence compared with conventional T2-weighted imaging, in a clinically acceptable time.

Significant Impact of Coffee Consumption on MR-Based Measures of Cardiac Function in a Population-Based Cohort Study without Manifest Cardiovascular Disease

Subclinical effects of coffee consumption (CC) with regard to metabolic, cardiac, and neurological complications were evaluated using a whole-body magnetic resonance imaging (MRI) protocol. A blended approach was used to estimate habitual CC in a population-based study cohort without a history of cardiovascular disease. Associations of CC with MRI markers of gray matter volume, white matter hyperintensities, cerebral microhemorrhages, total and visceral adipose tissue (VAT), hepatic proton density fat fraction, early/late diastolic filling rate, end-diastolic/-systolic and stroke volume, ejection fraction, peak ejection rate, and myocardial mass were evaluated by linear regression. In our analysis with 132 women and 168 men, CC was positively associated with MR-based cardiac function parameters including late diastolic filling rate, stroke volume (p < 0.01 each), and ejection fraction (p < 0.05) when adjusting for age, sex, smoking, hypertension, diabetes, Low-density lipoprotein (LDL), triglycerides, cholesterol, and alcohol consumption. CC was inversely associated with VAT independent of demographic variables and cardiovascular risk factors (p < 0.05), but this association did not remain significant after additional adjustment for alcohol consumption. CC was not significantly associated with potential neurodegeneration. We found a significant positive and independent association between CC and MRI-based systolic and diastolic cardiac function. CC was also inversely associated with VAT but not independent of alcohol consumption.

Machine-learning based exploration of determinants of gray matter volume in the KORA-MRI study

To identify the most important factors that impact brain volume, while accounting for potential collinearity, we used a data-driven machine-learning approach. Gray Matter Volume (GMV) was derived from magnetic resonance imaging (3T, FLAIR) and adjusted for intracranial volume (ICV). 93 potential determinants of GMV from the categories sociodemographics, anthropometric measurements, cardio-metabolic variables, lifestyle factors, medication, sleep, and nutrition were obtained from 293 participants from a population-based cohort from Southern Germany. Elastic net regression was used to identify the most important determinants of ICV-adjusted GMV. The four variables age (selected in each of the 1000 splits), glomerular filtration rate (794 splits), diabetes (323 splits) and diabetes duration (122 splits) were identified to be most relevant predictors of GMV adjusted for intracranial volume. The elastic net model showed better performance compared to a constant linear regression (mean squared error = 1.10 vs. 1.59, p < 0.001). These findings are relevant for preventive and therapeutic considerations and for neuroimaging studies, as they suggest to take information on metabolic status and renal function into account as potential confounders.

Hepatic fat is superior to BMI, visceral and pancreatic fat as a potential risk biomarker for neurodegenerative disease

OBJECTIVES

Prior studies relating body mass index (BMI) to brain volumes suggest an overall inverse association. However, BMI might not be an ideal marker, as it disregards different fat compartments, which carry different metabolic risks. Therefore, we analyzed MR-based fat depots and their association with gray matter (GM) volumes of brain structures, which show volumetric changes in neurodegenerative diseases.

METHODS

Warp-based automated brain segmentation of 3D FLAIR sequences was obtained in a population-based study cohort. Associations of temporal lobe, cingulate gyrus, and hippocampus GM volume with BMI and MR-based quantification of visceral adipose tissue (VAT), as well as hepatic and pancreatic proton density fat fraction (PDFF_hepatic and PDFF_panc, respectively), were assessed by linear regression.

RESULTS

In a sample of 152 women (age 56.2 ± 9.0 years) and 199 men (age 56.1 ± 9.1 years), we observed a significant inverse association of PDFF_hepatic and cingulate gyrus volume (p < 0.05) as well as of PDFF_hepatic and hippocampus volume (p < 0.05), when adjusting for age and sex. This inverse association was further enhanced for cingulate gyrus volume after additionally adjusting for hypertension, smoking, BMI, LDL, and total cholesterol (p < 0.01) and also alcohol (p < 0.01). No significant association was observed between PDFF_hepatic and temporal lobe and between temporal lobe, cingulate gyrus, or hippocampus volume and BMI, VAT, and PDFF_panc.

CONCLUSIONS

We observed a significant inverse, independent association of cingulate gyrus and hippocampus GM volume with hepatic fat, but not with other obesity measures. Increased hepatic fat could therefore serve as a marker of high-risk fat distribution.

KEY POINTS

• Obesity is associated with neurodegenerative processes. • In a population-based study cohort, hepatic fat was superior to BMI and visceral and pancreatic fat as a risk biomarker for decreased brain volume of cingulate gyrus and hippocampus. • Increased hepatic fat could serve as a marker of high-risk fat distribution.