BACKGROUND: Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level.

PURPOSE: To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters.

MATERIALS AND METHODS: We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FAIP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FAIP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R2OS) on the intra-tumor level and within-sample R2 across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FAIP, respectively.

RESULTS: Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R2OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FAIP (median R2OS = 0.31, 0.20-0.42). Samples with low R2OS for FAIP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R2 = 0.60) and FAIP (R2 = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FAIP is high in the presence of elongated and aligned cell structures, but low otherwise.

CONCLUSION: Cell density and structure anisotropy account for variability in MD and FAIP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

Diffusion MRI tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections. White matter parcellation classifies tractography streamlines into clusters or anatomically meaningful tracts. It enables quantification and visualization of whole-brain tractography. Currently, most parcellation methods focus on the deep white matter (DWM), whereas fewer methods address the superficial white matter (SWM) due to its complexity. We propose a novel two-stage deep-learning-based framework, Superficial White Matter Analysis (SupWMA), that performs an efficient and consistent parcellation of 198 SWM clusters from whole-brain tractography. A point-cloud-based network is adapted to our SWM parcellation task, and supervised contrastive learning enables more discriminative representations between plausible streamlines and outliers for SWM. We train our model on a large-scale tractography dataset including streamline samples from labeled long- and medium-range (over 40 mm) SWM clusters and anatomically implausible streamline samples, and we perform testing on six independently acquired datasets of different ages and health conditions (including neonates and patients with space-occupying brain tumors). Compared to several state-of-the-art methods, SupWMA obtains highly consistent and accurate SWM parcellation results on all datasets, showing good generalization across the lifespan in health and disease. In addition, the computational speed of SupWMA is much faster than other methods.

BACKGROUND AND OBJECTIVES: While chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological "brain age." We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.

METHODS: We extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.

RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years, r = 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; all p-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.

DISCUSSION: T2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

OBJECTIVE: Gray matter volume and glucose utilization have been reported to be reduced in the left subgenual cingulate of subjects with familial bipolar or unipolar depression. It is unclear whether these findings are secondary to recurrent illness or are part of a familial/genetic syndrome. The authors’ goal was to clarify these findings. METHOD: Volumetric analyses were performed by using magnetic resonance imaging in 41 patients experiencing their first episode of affective disorder or schizophrenia and in 20 normal comparison subjects. RESULTS: The left subgenual cingulate volume of the patients with affective disorder who had a family history of affective disorder was smaller than that of patients with affective disorder with no family history of the illness and the normal comparison subjects. Patients with schizophrenia did not differ from comparison subjects in left subgenual cingulate volume. CONCLUSIONS: Left subgenual cingulate abnormalities are present at first hospitalization for psychotic affective disorder in patients who have a family history of affective disorder.

OBJECTIVE: Studies of schizophrenia have not clearly defined handedness as a differentiating variable. Moreover, the relationship between thought disorder and anatomical anomalies has not been studied extensively in left-handed schizophrenic men. The twofold purpose of this study was to investigate gray matter volumes in the superior temporal gyrus of the temporal lobe (left and right hemispheres) in left-handed schizophrenic men and left-handed comparison men, in order to determine whether thought disorder in the left-handed schizophrenic men correlated with tissue volume abnormalities. METHOD: Left-handed male patients (N = 8) with DSM-III-R diagnoses of schizophrenia were compared with left-handed comparison men (N = 10) matched for age, socioeconomic status, and IQ. Magnetic resonance imaging (MRI) with a 1.5-T magnet was used to obtain scans, which consisted of contiguous 1.5-mm slices of the whole brain. MRI analyses (as previously defined by the authors) included the anterior, posterior, and total superior temporal gyrus in both the left and right hemispheres. RESULTS: There were three significant findings regarding the left-handed schizophrenic men: 1) bilaterally smaller gray matter volumes in the posterior superior temporal gyrus (16% smaller on the right, 15% smaller on the left); 2) a smaller volume on the right side of the total superior temporal gyrus; and 3) a positive correlation between thought disorder and tissue volume in the right anterior superior temporal gyrus. CONCLUSIONS: These results suggest that expression of brain pathology differs between left-handed and right-handed schizophrenic men and that the pathology is related to cognitive disturbance.