Kumiko Oishi

Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: Application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants

Diffusion tensor imaging (DTI) has been widely used to investigate the development of the neonatal and infant brain, and deviations related to various diseases or medical conditions like preterm birth. In this study, we created a probabilistic map of fiber pathways with known associated functions, on a published neonatal multimodal atlas. The pathways-of-interest include the superficial white matter (SWM) fibers just beneath the specific cytoarchitectonically defined cortical areas, which were difficult to evaluate with existing DTI analysis methods. The Jülich cytoarchitectonic atlas was applied to define cortical areas related to specific brain functions, and the Dynamic Programming (DP) method was applied to delineate the white matter pathways traversing through the SWM. Probabilistic maps were created for pathways related to motor, somatosensory, auditory, visual, and limbic functions, as well as major white matter tracts, such as the corpus callosum, the inferior fronto-occipital fasciculus, and the middle cerebellar peduncle, by delineating these structures in eleven healthy term-born neonates. In order to characterize maturation-related changes in diffusivity measures of these pathways, the probabilistic maps were then applied to DTIs of 49 healthy infants who were longitudinally scanned at three time-points, approximately five weeks apart. First, we investigated the normal developmental pattern based on 19 term-born infants. Next, we analyzed 30 preterm-born infants to identify developmental patterns related to preterm birth. Last, we investigated the difference in diffusion measures between these groups to evaluate the effects of preterm birth on the development of these functional pathways. Term-born and preterm-born infants both demonstrated a time-dependent decrease in diffusivity, indicating postnatal maturation in these pathways, with laterality seen in the corticospinal tract and the optic radiation. The comparison between term- and preterm-born infants indicated higher diffusivity in the preterm-born infants than in the term-born infants in three of these pathways: the body of the corpus callosum; the left inferior longitudinal fasciculus; and the pathway connecting the left primary/secondary visual cortices and the motion-sensitive area in the occipitotemporal visual cortex (V5/MT+). Probabilistic maps provided an opportunity to investigate developmental changes of each white matter pathway. Whether alterations in white matter pathways can predict functional outcomes will be further investigated in a follow-up study.

Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI

ABSTRACT Preterm birth adversely affects postnatal brain development. In order to investigate the critical gestational age at birth (GAB) that alters the developmental trajectory of gray and white matter structures in the brain, we investigated diffusion tensor and quantitative T2 mapping data in 43 term‐born and 43 preterm‐born infants. A novel multivariate linear model—the change point model, was applied to detect change points in fractional anisotropy, mean diffusivity, and T2 relaxation time. Change points captured the “critical” GAB value associated with a change in the linear relation between GAB and MRI measures. The analysis was performed in 126 regions across the whole brain using an atlas‐based image quantification approach to investigate the spatial pattern of the critical GAB. Our results demonstrate that the critical GABs are region‐ and modality‐specific, generally following a central‐to‐peripheral and bottom‐to‐top order of structural development. This study may offer unique insights into the postnatal neurological development associated with differential degrees of preterm birth. HighlightsDiffusion tensor and T2‐mapping MRI was measured from preterm and term‐born infants.Change point analysis detects critical gestational age at birth (GAB) in 126 brain regions.Change‐points maps revealed spatiotemporal order of structural development.GAB‐dependent MRI change reflected brain prematurity and postnatal development.

Delayed early developmental trajectories of white matter tracts of functional pathways in preterm-born infants: Longitudinal diffusion tensor imaging data.

Probabilistic maps of white matter pathways related to motor, somatosensory, auditory, visual, and limbic functions, and major white matter tracts (the corpus callosum, the inferior fronto-occipital fasciculus, and the middle cerebellar peduncle) were applied to evaluate the developmental trajectories of these tracts, using longitudinal diffusion tensor imaging (DTI) obtained in term-born and preterm-born healthy infants. Nineteen term-born and 30 preterm-born infants completed MR scans at three time points: Time-point 1, 41.6±2.7 postmenstrual weeks; Time-point 2, 46.0±2.9 postmenstrual weeks; and Time-point 3, 50.8±3.7 postmenstrual weeks. The DTI-derived scalar values (fractional anisotropy, eigenvalues, and radial diffusivity) of the three time points are available in this Data article.

Picturing the Size and Site of Stroke With an Expanded National Institutes of Health Stroke Scale

Background and Purpose— The National Institutes of Health Stroke Scale (NIHSS) includes minimal assessment of cognitive function, particularly in right hemisphere (RH) stroke. Descriptions of the Cookie Theft picture from the NIHSS allow analyses that (1) correlate with aphasia severity and (2) identify communication deficits in RH stroke. We hypothesized that analysis of the picture description contributes valuable information about volume and location of acute stroke. Methods— We evaluated 67 patients with acute ischemic stroke (34 left hemisphere [LH]; 33 RH) with the NIHSS, analysis of the Cookie Theft picture, and magnetic resonance imaging, compared with 35 sex- and age-matched controls. We evaluated descriptions for total content units (CU), syllables, ratio of left:right CU, CU/minute, and percent interpretive CU, based on previous studies. Lesion volume and percent damage to regions of interest were measured on diffusion-weighted imaging. Multivariable linear regression identified variables associated with infarct volume, independently of NIHSS score, age and sex. Results— Patients with RH and LH stroke differed from controls, but not from each other, on CU, syllables/CU, and CU/minute. Left:right CU was lower in RH compared with LH stroke. CU, syllables/CU, and NIHSS each correlated with lesion volume in LH and RH stroke. Lesion volume was best accounted by a model that included CU, syllables/CU, NIHSS, left:right CU, percent interpretive CU, and age, in LH and RH stroke. Each discourse variable and NIHSS score were associated with percent damage to different regions of interest, independently of lesion volume and age. Conclusions— Brief picture description analysis complements NIHSS scores in predicting stroke volume and location.

Macro- and Microstructural Changes in Patients with Spinocerebellar Ataxia Type 6: Assessment of Phylogenetic Subdivisions of the Cerebellum and the Brain Stem

BACKGROUND AND PURPOSE: Site-specific degeneration patterns of the infratentorial brain in relation to phylogenetic origins may relate to symptoms in patients with spinocerebellar degeneration, but the patterns are still unclear. We investigated macro- and microstructural changes of the infratentorial brain based on phylogenetic origins and their correlation with symptoms in patients with spinocerebellar ataxia type 6. MATERIALS AND METHODS: MR images of 9 patients with spinocerebellar ataxia type 6 and 9 age- and sex-matched controls were obtained. We divided the infratentorial brain on the basis of phylogenetic origins and performed an atlas-based analysis. Comparisons of the 2 groups and a correlation analysis assessed with the International Cooperative Ataxia Rating Scale excluding age effects were performed. RESULTS: A significant decrease of fractional volume and an increase of mean diffusivity were seen in all subdivisions of the cerebellum and in all the cerebellar peduncles except mean diffusivity in the inferior cerebellar peduncle in patients compared with controls (P < .0001 to <.05). The bilateral anterior lobes showed the strongest atrophy. Fractional volume decreased mainly in old regions, whereas mean diffusivity increased mainly in new regions of the cerebellum. Reflecting this tendency, the International Cooperative Ataxia Rating Scale total score showed strong correlations in fractional volume in the right flocculonodular lobe and the bilateral deep structures and in mean diffusivity in the bilateral posterior lobes (r = 0.73 to ±0.87). CONCLUSIONS: We found characteristic macro- and microstructural changes, depending on phylogenetic regions of the infratentorial brain, that strongly correlated with clinical symptoms in patients with spinocerebellar ataxia type 6.

Change-point analysis data of neonatal diffusion tensor MRI in preterm and term-born infants

The data presented in this article are related to the research article entitled “Mapping the Critical Gestational Age at Birth that Alters Brain Development in Preterm-born Infants using Multi-Modal MRI” (Wu et al., 2017) [1]. Brain immaturity at birth poses critical neurological risks in the preterm-born infants. We used a novel change-point model to analyze the critical gestational age at birth (GAB) that could affect postnatal development, based on diffusion tensor MRI (DTI) acquired from 43 preterm and 43 term-born infants in 126 brain regions. In the corresponding research article, we presented change-point analysis of fractional anisotropy (FA) and mean diffusivities (MD) measurements in these infants. In this article, we offered the relative changes of axonal and radial diffusivities (AD and RD) in relation to the change of FA and FA-based change-points, and we also provided the AD- and RD-based change-point results.

Gross feature recognition of Anatomical Images based on Atlas grid (GAIA): Incorporating the local discrepancy between an atlas and a target image to capture the features of anatomic brain MRI

We aimed to develop a new method to convert T1-weighted brain MRIs to feature vectors, which could be used for content-based image retrieval (CBIR). To overcome the wide range of anatomical variability in clinical cases and the inconsistency of imaging protocols, we introduced the Gross feature recognition of Anatomical Images based on Atlas grid (GAIA), in which the local intensity alteration, caused by pathological (e.g., ischemia) or physiological (development and aging) intensity changes, as well as by atlas–image misregistration, is used to capture the anatomical features of target images. As a proof-of-concept, the GAIA was applied for pattern recognition of the neuroanatomical features of multiple stages of Alzheimers disease, Huntingtons disease, spinocerebellar ataxia type 6, and four subtypes of primary progressive aphasia. For each of these diseases, feature vectors based on a training dataset were applied to a test dataset to evaluate the accuracy of pattern recognition. The feature vectors extracted from the training dataset agreed well with the known pathological hallmarks of the selected neurodegenerative diseases. Overall, discriminant scores of the test images accurately categorized these test images to the correct disease categories. Images without typical disease-related anatomical features were misclassified. The proposed method is a promising method for image feature extraction based on disease-related anatomical features, which should enable users to submit a patient image and search past clinical cases with similar anatomical phenotypes.

Impaired Recognition of Emotional Faces after Stroke Involving Right Amygdala or Insula

Abstract Despite its basic and translational importance, the neural circuitry supporting the perception of emotional faces remains incompletely understood. Functional imaging studies and chronic lesion studies indicate distinct roles of the amygdala and insula in recognition of fear and disgust in facial expressions, whereas intracranial encephalography studies, which are not encumbered by variations in human anatomy, indicate a somewhat different role of these structures. In this article, we leveraged lesion‐mapping techniques in individuals with acute right hemisphere stroke to investigate lesions associated with impaired recognition of prototypic emotional faces before significant neural reorganization can occur during recovery from stroke. Right hemisphere stroke patients were significantly less accurate than controls on a test of emotional facial recognition for both positive and negative emotions. Patients with right amygdala or anterior insula lesions had significantly lower scores than other right hemisphere stroke patients on recognition of angry and happy faces. Lesion volume within several regions, including the right amygdala and anterior insula, each independently contributed to the error rate in recognition of individual emotions. Results provide additional support for a necessary role of the right amygdala and anterior insula within a network of regions underlying recognition of facial expressions, particularly those that have biological importance or motivational relevance and have implications for clinical practice.

Stroke of bad luck

ABSTRACT We hypothesized that distinct acute right hemisphere lesions disrupt separate components of valuation and emotional response to winning and losing money and of emotional empathy in observing a partner win or lose money. We measured skin conductance response (SCR) and ratings of emotions when acute right hemisphere stroke patients or healthy controls won or lost money in roulette, or when they watched a partner win or lose. Our results showed that percentage of damage after stroke to right anterior insula and frontal operculum negatively correlated with both SCR to winning and losing and difference between rating wins versus losses.

Multi-contrast Large Deformation Diffeomorphic Metric Mapping and Diffusion Tensor Image Registration

* Center for Imaging Science, The Whiting School of Engineering, The Johns Hopkins University, 301 Clark Hall, Baltimore, MD † Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD § F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD ¶ Department of Biomedical Engineering, The Johns Hopkins University School of Medicine, Baltimore, MD Large Deformation Diffeomorphic Metric Mapping (LDDMM) is a nonlinear registration algorithm which calculates diffeomorphic transformations between scalar images [1]. The diffeomorphisms are invertible and smooth maps with a smooth inverse. In this work, we extend this algorithm to multi-contrast LDDMM (mc-LDDMM) for inter-subject registration of diffusion tensor images (DTI) [2]. Different scalar valued isotropy and anisotropy images obtained from DTI data were used to drive the registration with their different contrast information.