ABSTRACT
Recent studies have shown that white-gray contrast (WGC) of either cortical or subcortical gray matter provides for accurate predictions of age in typically developing (TD) children, and that, at least for the cortex, it changes differently with age in subjects with autism spectrum disorder (ASD) compared to their TD peers. Our previous study showed different patterns of contrast change between ASD and TD in sensorimotor and association cortices. While that study was confined to the cortex, we hypothesized that subcortical structures, particularly the thalamus, were involved in the observed cortical dichotomy between lower and higher processing. The current paper investigates that hypothesis using the WGC measures from the thalamus in addition to those from the cortex. We compared age-related WGC changes in the thalamus to those in the cortex. To capture the simultaneity of this change across the two structures, we devised a metric capturing the co-development of the thalamus and cortex (CoDevTC), proportional to the magnitude of cortical and thalamic age-related WGC change. We calculated this metric for each of the subjects in a large homogeneous sample taken from the Autism Brain Imaging Data Exchange (ABIDE) (N = 434). We used structural MRI data from the largest high-quality cross-sectional sample (NYU) as well as two other large high-quality sites, GU and OHSU, all three using Siemens 3T scanners. We observed that the co-development features in ASD and TD exhibit contrasting patterns; specifically, some higher-order thalamic nuclei, such as the lateral dorsal nucleus, exhibited reduction in codevelopment with most of the cortex in ASD compared to TD. Moreover, this difference in the CoDevTC pattern correlates with a number of behavioral measures across multiple cognitive and physiological domains. The results support previous notions of altered connectivity in autism, but add more specific evidence about the heterogeneity in thalamocortical development that elucidates the mechanisms underlying the clinical features of ASD.
Subject(s)
Autism Spectrum Disorder , Autistic Disorder , Child , Humans , Cross-Sectional Studies , Thalamus , Magnetic Resonance ImagingABSTRACT
Exposures to prenatal maternal depressive symptoms (PMDS) may lead to neurodevelopmental changes in the offspring in a sex-dependent way. Although a connection between PMDS and infant brain development has been established by earlier studies, the relationship between PMDS exposures measured at various prenatal stages and microstructural alterations in fundamental subcortical structures such as the amygdala remains unknown. In this study, we investigated the associations between PMDS measured during gestational weeks 14, 24 and 34 and infant amygdala microstructural properties using diffusion tensor imaging. We explored amygdala mean diffusivity (MD) alterations in response to PMDS in infants aged 11 to 54 days from birth. PMDS had no significant main effect on the amygdala MD metrics. However, there was a significant interaction effect for PMDS and infant sex in the left amygdala MD. Compared with girls, boys exposed to greater PMDS during gestational week 14 showed significantly higher left amygdala MD. These results indicate that PMDS are linked to infants' amygdala microstructure in boys. These associations may be relevant to later neuropsychiatric outcomes in the offspring. Further research is required to better understand the mechanisms underlying these associations and to develop effective interventions to counteract any potential adverse consequences.
Subject(s)
Diffusion Tensor Imaging , White Matter , Infant, Newborn , Male , Infant , Female , Pregnancy , Humans , Diffusion Tensor Imaging/methods , Depression/diagnostic imaging , Amygdala/diagnostic imaging , Brain , Diffusion Magnetic Resonance ImagingABSTRACT
Blood-flow artifacts present a serious challenge for most, if not all, volumetric analytical approaches. We utilize T1-weighted data with prominent blood-flow artifacts from the Autism Brain Imaging Data Exchange (ABIDE) multisite agglomerative dataset to assess the impact that such blood-flow artifacts have on registration of T1-weighted data to a template. We use a heuristic approach to identify the blood-flow artifacts in these data; we use the resulting blood masks to turn the underlying voxels to the intensity of the cerebro-spinal fluid, thus mimicking the effect of blood suppression. We then register both the original data and the deblooded data to a common T1-weighted template, and compare the quality of those registrations to the template in terms of similarity to the template. The registrations to the template based on the deblooded data yield significantly higher similarity values compared with those based on the original data. Additionally, we measure the nonlinear deformations needed to transform the data from the position achieved by registering the original data to the template to the position achieved by registering the deblooded data to the template. The results indicate that blood-flow artifacts may seriously impact data processing that depends on registration to a template, that is, most all data processing.
Subject(s)
Autistic Disorder , Magnetic Resonance Imaging , Humans , Magnetic Resonance Imaging/methods , Brain/diagnostic imaging , Imaging, Three-Dimensional/methods , Artifacts , Image Processing, Computer-Assisted/methods , AlgorithmsABSTRACT
Brain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6-12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.
Subject(s)
Autism Spectrum Disorder/diagnosis , Autism Spectrum Disorder/pathology , Brain/growth & development , Brain/pathology , Autism Spectrum Disorder/genetics , Autism Spectrum Disorder/psychology , Child, Preschool , Family Health , Female , Humans , Infant , Longitudinal Studies , Male , Neuroimaging , Prognosis , Risk , Social BehaviorABSTRACT
Prenatal adversity has been linked to later psychopathology. Yet, research on cumulative prenatal adversity, as well as its interaction with offspring genotype, on brain and behavioral development is scarce. With this study, we aimed to address this gap. In Finnish mother-infant dyads, we investigated the association of a cumulative prenatal adversity sum score (PRE-AS) with (a) child emotional and behavioral problems assessed with the Strengths and Difficulties Questionnaire at 4 and 5 years (N = 1568, 45.3% female), (b) infant amygdalar and hippocampal volumes (subsample N = 122), and (c) its moderation by a hippocampal-specific coexpression polygenic risk score based on the serotonin transporter (SLC6A4) gene. We found that higher PRE-AS was linked to greater child emotional and behavioral problems at both time points, with partly stronger associations in boys than in girls. Higher PRE-AS was associated with larger bilateral infant amygdalar volumes in girls compared to boys, while no associations were found for hippocampal volumes. Further, hyperactivity/inattention in 4-year-old girls was related to both genotype and PRE-AS, the latter partially mediated by right amygdalar volumes as preliminary evidence suggests. Our study is the first to demonstrate a dose-dependent sexually dimorphic relationship between cumulative prenatal adversity and infant amygdalar volumes.
ABSTRACT
Linear registration to stereotaxic space is a common first step in many automated image-processing tools for analysis of human brain MRI scans. This step is crucial for the success of the subsequent image-processing steps. Several well-established algorithms are commonly used in the field of neuroimaging for this task, but none have a 100% success rate. Manual assessment of the registration is commonly used as part of quality control. To reduce the burden of this time-consuming step, we propose Deep Automated Registration Qc (DARQ), a fully automatic quality control method based on deep learning that can replace the human rater and accurately perform quality control assessment for stereotaxic registration of T1w brain scans. In a recently published study from our group comparing linear registration methods, we used a database of 9325 MRI scans and 64476 registrations from several publicly available datasets and applied seven linear registration tools to them. In this study, the resulting images that were assessed and labeled by a human rater are used to train a deep neural network to detect cases when registration failed. We further validated the results on an independent dataset of patients with multiple sclerosis, with manual QC labels available (n=1200). In terms of agreement with a manual rater, our automated QC method was able to achieve 89% accuracy and 85% true negative rate (equivalently 15% false positive rate) in detecting scans that should pass quality control in a balanced cross-validation experiments, and 96.1% accuracy and 95.5% true negative rate (or 4.5% FPR) when evaluated in a balanced independent sample, similar to manual QC rater (test-retest accuracy of 93%). The results show that DARQ is robust, fast, accurate, and generalizable in detecting failure in linear stereotaxic registrations and can substantially reduce QC time (by a factor of 20 or more) when processing large datasets.
Subject(s)
Deep Learning , Brain/diagnostic imaging , Humans , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Quality ControlABSTRACT
Both cortical and subcortical structures are organized into a large number of distinct areas reflecting functional and cytoarchitectonic differences. Mapping these areas is of fundamental importance to neuroscience. A central obstacle to this task is the inaccuracy associated with bringing results from individuals into a common space. The vast individual differences in morphology pose a serious problem for volumetric registration. Surface-based approaches fare substantially better, but have thus far been used only for cortical parcellation, leaving subcortical parcellation in volumetric space. We extend the surface-based approach to include also the subcortical deep gray-matter structures, thus achieving a uniform representation across both cortex and subcortex, suitable for use with surface-based metrics that span these structures, for example, white/gray contrast. Using data from the Enhanced Nathan Klein Institute-Rockland Sample, limited to individuals between 19 and 69 years of age, we generate a functional parcellation of both the cortical and subcortical surfaces. To assess this extended parcellation, we show that (a) our parcellation provides greater homogeneity of functional connectivity patterns than do arbitrary parcellations matching in the number and size of parcels; (b) our parcels align with known cortical and subcortical architecture; and (c) our extended functional parcellation provides an improved fit to the complexity of life-span (6-85 years) changes in white/gray contrast data compared to arbitrary parcellations matching in the number and size of parcels, supporting its use with surface-based measures. We provide our extended functional parcellation for the use of the neuroimaging community.
Subject(s)
Cerebral Cortex/diagnostic imaging , Connectome , Gray Matter/diagnostic imaging , White Matter/diagnostic imaging , Adult , Aged , Female , Humans , Image Processing, Computer-Assisted , Magnetic Resonance Imaging , Male , Middle Aged , Models, Theoretical , Young AdultABSTRACT
The corpus callosum (CC) is the largest fiber tract in the human brain, allowing interhemispheric communication by connecting homologous areas of the two cerebral hemispheres. In adults, CC size shows a robust allometric relationship with brain size, with larger brains having larger callosa, but smaller brains having larger callosa relative to brain size. Such an allometric relationship has been shown in both males and females, with no significant difference between the sexes. But there is some evidence that there are alterations in these allometric relationships during development. However, it is currently not known whether there is sexual dimorphism in these allometric relationships from birth, or if it only develops later. We study this in neonate data. Our results indicate that there are already sex differences in these allometric relationships in neonates: male neonates show the adult-like allometric relationship between CC size and brain size; however female neonates show a significantly more positive allometry between CC size and brain size than either male neonates or female adults. The underlying cause of this sexual dimorphism is unclear; but the existence of this sexual dimorphism in neonates suggests that sex-differences in lateralization have prenatal origins.
Subject(s)
Corpus Callosum , Sex Characteristics , Adult , Brain/diagnostic imaging , Corpus Callosum/diagnostic imaging , Female , Humans , Infant, Newborn , MaleABSTRACT
Cerebellar symptoms in multiple sclerosis (MS) are well described; however, the exact contribution of cerebellar damage to MS disability has not been fully explored. Longer-term observational periods are necessary to better understand the dynamics of pathological changes within the cerebellum and their clinical consequences. Cerebellar lobe and single lobule volumes were automatically segmented on 664 3D-T1-weighted MPRAGE scans (acquired at a single 1.5 T scanner) of 163 MS patients (111 women; mean age: 47.1 years; 125 relapsing-remitting (RR) and 38 secondary progressive (SP) MS, median EDSS: 3.0) imaged annually over 4 years. Clinical scores (EDSS, 9HPT, 25FWT, PASAT, SDMT) were determined per patient per year with a maximum clinical follow-up of 11 years. Linear mixed-effect models were applied to assess the association between cerebellar volumes and clinical scores and whether cerebellar atrophy measures may predict future disability progression. SPMS patients exhibited faster posterior superior lobe volume loss over time compared to RRMS, which was related to increase of EDSS over time. In RRMS, cerebellar volumes were significant predictors of motor scores (e.g. average EDSS, T25FWT and 9HPT) and SDMT. Atrophy of motor-associated lobules (IV-VI + VIII) was a significant predictor of future deterioration of the 9HPT of the non-dominant hand. In SPMS, the atrophy rate of the posterior superior lobe (VI + Crus I) was a significant predictor of future PASAT performance deterioration. Regional cerebellar volume reduction is associated with motor and cognitive disability in MS and may serve as a predictor for future disease progression, especially of dexterity and impaired processing speed.
Subject(s)
Multiple Sclerosis, Chronic Progressive , Multiple Sclerosis , Atrophy/pathology , Cerebellum/diagnostic imaging , Cerebellum/pathology , Disability Evaluation , Female , Humans , Magnetic Resonance Imaging , Middle Aged , Multiple Sclerosis/pathology , Multiple Sclerosis, Chronic Progressive/diagnostic imaging , Multiple Sclerosis, Chronic Progressive/pathologyABSTRACT
Previous literature links maternal pregnancy-specific anxiety (PSA) with later difficulties in child emotional and social cognition as well as memory, functions closely related to the amygdala and the hippocampus. Some evidence also suggests that PSA affects child amygdalar volumes in a sex-dependent way. However, no studies investigating the associations between PSA and newborn amygdalar and hippocampal volumes have been reported. We investigated the associations between PSA and newborn amygdalar and hippocampal volumes and whether associations are sex-specific in 122 healthy newborns (68 males/54 females) scanned at 2-5 weeks postpartum. PSA was measured at gestational week 24 with the Pregnancy-Related Anxiety Questionnaire Revised 2 (PRAQ-R2). The associations were analyzed with linear regression controlling for confounding variables. PSA was associated positively with left amygdalar volume in girls, but no significant main effect was found in the whole group or in boys. No significant main or sex-specific effect was found for hippocampal volumes. Although this was an exploratory study, the findings suggest a sexually dimorphic association of mid-pregnancy PSA with newborn amygdalar volumes.
Subject(s)
Birth Cohort , Prostate-Specific Antigen , Amygdala/diagnostic imaging , Anxiety , Child , Cohort Studies , Female , Hippocampus/diagnostic imaging , Humans , Infant, Newborn , Magnetic Resonance Imaging , Male , Pregnancy , Stress, PsychologicalABSTRACT
BACKGROUND: Brain volume loss measured from magnetic resonance imaging (MRI) is a marker of neurodegeneration and predictor of disability progression in MS, and is commonly used to assess drug efficacy at the group level in clinical trials. Whether measures of brain volume loss could be useful to help guide management of individual patients depends on the relative magnitude of the changes over a given interval to physiological and technical sources of variability. GOAL: To understand the relative contributions of neurodegeneration vs. physiological and technical sources of variability to measurements of brain volume loss in individuals. MATERIAL AND METHODS: Multiple T1-weighted 3D MPRAGE images were acquired from a healthy volunteer and MS patient over varying time intervals: 7 times on the first day (before breakfast at 7:30AM and then every 2 âh for 12 âh), each day for the next 6 working days, and 6 times over the remainder of the year, on 2 âSiemens MRI scanners: 1.5T Sonata (S1) and 3.0T TIM Trio (S2). Scan-reposition-rescan data were acquired on S2 for daily, monthly and 1-year visits. Percent brain volume change (PBVC) was measured from baseline to each follow-up scan using FSL/SIENA. We estimated the effect of physiologic fluctuations on brain volume using linear regression of the PBVC values over hourly and daily intervals. The magnitude of the physiological effect was estimated by comparing the root-mean-square error (RMSE) of the regression of all the data points relative to the regression line, for the hourly scans vs the daily scans. Variance due to technical sources was assessed as the RMSE of the regression over time using the intracranial volume as a reference. RESULTS: The RMSE of PBVC over 12 âh, for both scanners combined, ("Hours", 0.15%), was similar to the day-to-day variation over 1 week ("Days", 0.14%), and both were smaller than the RMS error over the year (0.21%). All of these variations, however, were smaller than the scan-reposition-rescan RMSE (0.32%). The variability of PBVC for the individual scanners followed the same trend. The standard error of the mean (SEM) for PBVC was 0.26 for S1, and 0.22 for S2. From these values, we computed the minimum detectable change (MDC) to be 0.7% on S1 and 0.6% on S2. The location of the brain along the z-axis of the magnet inversely correlated with brain volume change for hourly and daily brain volume fluctuations (p â< â0.01). CONCLUSION: Consistent diurnal brain volume fluctuations attributable to physiological shifts were not detectable in this small study. Technical sources of variation dominate measured changes in brain volume in individuals until the volume loss exceeds around 0.6-0.7%. Reliable interpretation of measured brain volume changes as pathological (greater than normal aging) in individuals over 1 year requires changes in excess of about 1.1% (depending on the scanner). Reliable brain atrophy detection in an individual may be feasible if the rate of brain volume loss is large, or if the measurement interval is sufficiently long.
Subject(s)
Brain/pathology , Image Processing, Computer-Assisted/methods , Magnetic Resonance Imaging/methods , Multiple Sclerosis/pathology , Neuroimaging/methods , Adult , Atrophy/diagnostic imaging , Atrophy/pathology , Brain/diagnostic imaging , Healthy Volunteers , Humans , Male , Multiple Sclerosis/diagnostic imagingABSTRACT
Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1-weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration-derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labeled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and subcortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state-of-the-art accuracies and a high robustness to outliers. Further validation on a multistructure segmentation task in a scan-rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan-rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g., requiring only 10 s for left/right hippocampal segmentation in 1 × 1 × 1 mm3 MNI stereotaxic space), orders of magnitude faster than conventional multiatlas segmentation methods.
Subject(s)
Brain/anatomy & histology , Brain/diagnostic imaging , Deep Learning , Image Interpretation, Computer-Assisted , Magnetic Resonance Imaging , Neuroimaging , Adult , Aged , Humans , Image Interpretation, Computer-Assisted/methods , Image Interpretation, Computer-Assisted/standards , Magnetic Resonance Imaging/methods , Magnetic Resonance Imaging/standards , Middle Aged , Neuroimaging/methods , Neuroimaging/standards , Reproducibility of ResultsABSTRACT
Polygenic risk scores for major depressive disorder (PRS-MDD) have been identified in large genome-wide association studies, and recent findings suggest that PRS-MDD might interact with environmental risk factors to shape human limbic brain development as early as in the prenatal period. Striatal structures are crucially involved in depression; however, the association of PRS-MDD with infant striatal volumes is yet unknown. In this study, 105 Finnish mother-infant dyads (44 female, 11-54 days old) were investigated to reveal how infant PRS-MDD is associated with infant dorsal striatal volumes (caudate, putamen) and whether PRS-MDD interacts with prenatal maternal depressive symptoms (Edinburgh Postnatal Depression Scale, gestational weeks 14, 24, 34) on infant striatal volumes. A robust sex-specific main effect of PRS-MDD on bilateral infant caudate volumes was observed. PRS-MDD were more positively associated with caudate volumes in boys compared to girls. No significant interaction effects of genotype PRS-MDD with the environmental risk factor "prenatal maternal depressive symptoms" (genotype-by-environment interaction) nor significant interaction effects of genotype with prenatal maternal depressive symptoms and sex (genotype-by-environment-by-sex interaction) were found for infant dorsal striatal volumes. Our study showed that a higher PRS-MDD irrespective of prenatal exposure to maternal depressive symptoms is associated with smaller bilateral caudate volumes, an indicator of greater susceptibility to major depressive disorder, in female compared to male infants. This sex-specific polygenic effect might lay the ground for the higher prevalence of depression in women compared to men.
Subject(s)
Caudate Nucleus/diagnostic imaging , Depressive Disorder, Major/diagnostic imaging , Depressive Disorder, Major/genetics , Multifactorial Inheritance/genetics , Pregnancy Complications/diagnostic imaging , Pregnancy Complications/genetics , Sex Characteristics , Adult , Cohort Studies , Depressive Disorder, Major/epidemiology , Female , Finland/epidemiology , Genetic Predisposition to Disease/epidemiology , Genetic Predisposition to Disease/genetics , Humans , Infant , Infant, Newborn , Male , Pregnancy , Pregnancy Complications/epidemiologyABSTRACT
The maturational schedule of typical brain development is tightly constrained; deviations from it are associated with cognitive atypicalities, and are potentially predictive of developmental disorders. Previously, we have shown that the white/gray contrast at the inner border of the cortex is a good predictor of chronological age, and is sensitive to aspects of brain development that reflect cognitive performance. Here we extend that work to include the white/gray contrast at the border of subcortical structures. We show that cortical and subcortical contrast together yield better age-predictions than any non-kernel-based method based on a single image-type, and that the residuals of the improved predictions provide new insight into unevenness in cognitive performance. We demonstrate the improvement in age predictions in two large datasets: the NIH Pediatric Data, with 831 scans of typically developing individuals between 4 and 22 years of age; and the Pediatric Imaging, Neurocognition, and Genetics data, with 909 scans of individuals in a similar age-range. Assessment of the relation of the residuals of these age predictions to verbal and performance IQ revealed correlations in opposing directions, and a principal component analysis of the residuals of the model that best fit the contrast data produced components related to either performance IQ or verbal IQ. Performance IQ was associated with the first principle component, reflecting increased cortical contrast, broadly, with almost no subcortical presence; verbal IQ was associated with the second principle component, reflecting reduced contrast in the basal ganglia and increased contrast in the bilateral arcuate fasciculi.
Subject(s)
Aging , Brain/anatomy & histology , Brain/growth & development , Cognition/physiology , Gray Matter/anatomy & histology , Gray Matter/growth & development , White Matter/anatomy & histology , White Matter/growth & development , Adolescent , Adult , Child , Child, Preschool , Female , Humans , Image Processing, Computer-Assisted , Intelligence , Magnetic Resonance Imaging , Male , Young AdultABSTRACT
In magnetic resonance (MR) imaging studies of child brain development, structural brain atlases usually serve as important references for the pediatric population, in which individual images are spatially normalized into a common or standard stereotactic space. However, the popular existing pediatric brain atlases (e.g., National Institutes of Health pediatric atlases, NIH-PD) are mostly based on MR images obtained from Caucasian populations and thus are not ideal for the characterization of the brains of Chinese children due to neuroanatomical differences related to genetic and environmental factors. Here, we use an unbiased template construction algorithm to create a set of age-specific Chinese pediatric (CHN-PD) atlases based on high-quality T1-and T2-weighted MR images from 328 cognitively normal Chinese children aged 6-12 years. The CHN-PD brain atlases include asymmetric and symmetric templates, sex-specific templates and tissue probability templates, and contain multiple age-specific templates at one-year intervals. A direct comparison of the CHN-PD and NIH-PD atlases reveals dramatic anatomical differences mainly in the bilateral frontal and parietal regions. After applying the CHN-PD and NIH-PD atlases to two independent Chinese pediatric datasets (Nâ¯=â¯114 and Nâ¯=â¯71), we find that the CHN-PD atlases result in significantly higher accuracy than the NIH-PD atlases in both predicting "brain age" and guiding brain tissue segmentation. These results suggest that the CHN-PD brain atlases are necessary for studies of the typical and atypical development of the Chinese pediatric population. These CHN-PD atlases have been released on the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website (https://www.nitrc.org/projects/chn-pd).
Subject(s)
Atlases as Topic , Brain/anatomy & histology , Neuroimaging/methods , Asian People , Brain/diagnostic imaging , Child , China , Female , Humans , Magnetic Resonance Imaging , MaleABSTRACT
Most of human genome is present in two copies (maternal and paternal). However, segments of the genome can be deleted or duplicated, and many of these genomic variations (known as Copy Number Variants) are associated with psychiatric disorders. 16p11.2 copy number variants (breakpoint 4-5) confer high risk for neurodevelopmental disorders and are associated with structural brain alterations of large effect-size. Methods used in previous studies were unable to investigate the onset of these alterations and whether they evolve with age. In this study, we aim at characterizing age-related effects of 16p11.2 copy number variants by analyzing a group with a broad age range including younger individuals. A large normative developmental dataset was used to accurately adjust for effects of age. We normalized volumes of segmented brain regions as well as volumes of each voxel defined by tensor-based morphometry. Results show that the total intracranial volumes, the global gray and white matter volumes are respectively higher and lower in deletion and duplication carriers compared to control subjects at 4.5 years of age. These differences remain stable through childhood, adolescence and adulthood until 23 years of age (range: 0.5 to 1.0 Z-score). Voxel-based results are consistent with previous findings in 16p11.2 copy number variant carriers, including increased volume in the calcarine cortex and insula in deletions, compared to controls, with an inverse effect in duplication carriers (1.0 Z-score). All large effect-size voxel-based differences are present at 4.5 years and seem to remain stable until the age of 23. Our results highlight the stability of a neuroimaging endophenotype over 2 decades during which neurodevelopmental symptoms evolve at a rapid pace.
Subject(s)
Brain/growth & development , Brain/pathology , Chromosome Deletion , Chromosome Duplication , Chromosomes, Human, Pair 16/genetics , DNA Copy Number Variations/genetics , Adolescent , Adult , Child , Child, Preschool , Humans , Young AdultABSTRACT
BACKGROUND: Harmonized protocols to collect imaging data must be devised, employed, and maintained in multicentric studies to reduce interscanner variability in subsequent analyses. PURPOSE: To present a standardized protocol for multicentric research on dementia linked to neurodegeneration in aging, harmonized on all three major vendor platforms. The protocol includes a common procedure for qualification, quality control, and quality assurance and feasibility in large-scale studies. STUDY TYPE: Prospective. SUBJECTS: The study involved a geometric phantom, a single individual volunteer, and 143 cognitively healthy, mild cognitively impaired, and Alzheimer's disease participants in a large-scale, multicentric study. FIELD STRENGTH/SEQUENCES: MRI was perform with 3T scanners (GE, Philips, Siemens) and included 3D T1 w, PD/T2 w, T2* , T2 w-FLAIR, diffusion, and BOLD resting state acquisitions. ASSESSMENT: Measures included signal- and contrast-to-noise ratios (SNR and CNR, respectively), total brain volumes, and total scan time. STATISTICAL TESTS: SNR, CNR, and scan time were compared between scanner vendors using analysis of variance (ANOVA) and Tukey tests, while brain volumes were tested using linear mixed models. RESULTS: Geometric phantom T1 w SNR was significantly (P < 0.001) higher in Philips (mean: 71.4) than Siemens (29.5), while no significant difference was observed between vendors for T2 w (32.0 and 37.2, respectively, P = 0.243). Single individual volunteer T1 w CNR was higher in subcortical regions for Siemens (P < 0.001), while Philips had higher cortical CNR (P = 0.044). No significant difference in brain volumes was observed between vendors (P = 0.310/0.582/0.055). The average scan time was 41.0 minutes (SD: 2.8) and was not significantly different between sites (P = 0.071) and cognitive groups (P = 0.853). DATA CONCLUSION: The harmonized Canadian Dementia Imaging Protocol suits the needs of studies that need to ensure quality MRI data acquisition for the measurement of brain changes across adulthood, due to aging, neurodegeneration, and other etiologies. A detailed description, exam cards, and operators' manual are freely available at the following site: www.cdip-pcid.ca. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:456-465.
Subject(s)
Aging , Alzheimer Disease/diagnostic imaging , Dementia/diagnostic imaging , Magnetic Resonance Imaging/standards , Neurodegenerative Diseases/diagnostic imaging , Algorithms , Brain/diagnostic imaging , Canada/epidemiology , Humans , Linear Models , Phantoms, Imaging , Prospective Studies , Quality Assurance, Health Care , Quality Control , Reproducibility of Results , Signal-To-Noise RatioABSTRACT
OBJECTIVE: To determine the frequency of cortical lesions (CLs) in patients with pediatric-onset multiple sclerosis (POMS) using multi-contrast magnetic resonance imaging (MRI), and the relationship between frontal CL load and upper limb dexterity assessed with the Nine-Hole Peg Test (9-HPT). METHODS: Participants completed the 9-HPT and were imaged on a 3T MRI scanner to collect T1-weighted three-dimensional (3D) magnetization prepared rapid gradient echo (MPRAGE), proton density-weighted, T2-weighted and fluid-attenuated inversion recovery (FLAIR) images. CLs were manually segmented using all MRI contrasts. RESULTS: We enrolled 24 participants with POMS (mean (standard deviation) age at first symptom: 13.3 (±2.7) years; mean age at scan: 18.8 (±3) years; mean disease duration of 5 (±3.2) years). A total of 391 CLs (mean, 16.3 ± 27.2; median, 7) were identified in 19 of 24 POMS patients (79%). The total number of CLs was positively associated with white matter lesion volume ( p = 0.04) but not with thalamic volume, age at the time of the scan, or disease duration. The number of frontal CLs was associated with slower performance on the 9-HPT ( p = 0.05). CONCLUSION: Multi-contrast 3T MRI led to a high rate of CL detection, demonstrating that cortical pathology occurs even in pediatric-onset disease. Frontal lobe CL count was associated with reduced manual dexterity, indicating that these CLs are clinically relevant.
Subject(s)
Cerebral Cortex/pathology , Hand/physiopathology , Motor Skills/physiology , Neuroimaging/methods , White Matter/pathology , White Matter/physiopathology , Adolescent , Cerebral Cortex/diagnostic imaging , Child , Female , Frontal Lobe/diagnostic imaging , Frontal Lobe/pathology , Humans , Magnetic Resonance Imaging , Male , White Matter/diagnostic imagingABSTRACT
Imaging biomarkers are frequently proposed as endpoints for clinical trials targeting brain amyloidosis in Alzheimer's disease (AD); however, the specific impact of amyloid-ß (Aß) aggregation on biomarker abnormalities remains elusive in AD. Using the McGill-R-Thy1-APP transgenic rat as a model of selective Aß pathology, we characterized the longitudinal progression of abnormalities in biomarkers commonly used in AD research. Middle-aged (9-11 months) transgenic animals (both male and female) displayed mild spatial memory impairments and disrupted cingulate network connectivity measured by resting-state fMRI, even in the absence of hypometabolism (measured with PET [18F]FDG) or detectable fibrillary amyloidosis (measured with PET [18F]NAV4694). At more advanced ages (16-19 months), cognitive deficits progressed in conjunction with resting connectivity abnormalities; furthermore, hypometabolism, Aß plaque accumulation, reduction of CSF Aß1-42 concentrations, and hippocampal atrophy (structural MRI) were detectable at this stage. The present results emphasize the early impact of Aß on brain connectivity and support a framework in which persistent Aß aggregation itself is sufficient to impose memory circuits dysfunction, which propagates to adjacent brain networks at later stages.SIGNIFICANCE STATEMENT The present study proposes a "back translation" of the Alzheimer pathological cascade concept from human to animals. We used the same set of Alzheimer imaging biomarkers typically used in large human cohorts and assessed their progression over time in a transgenic rat model, which allows for a finer spatial resolution not attainable with mice. Using this translational platform, we demonstrated that amyloid-ß pathology recapitulates an Alzheimer-like profile of biomarker abnormalities even in the absence of other hallmarks of the disease such as neurofibrillary tangles and widespread neuronal losses.
Subject(s)
Alzheimer Disease/pathology , Amyloid beta-Peptides/analysis , Brain/pathology , Disease Models, Animal , Magnetic Resonance Imaging/methods , Memory Disorders/pathology , Multimodal Imaging/methods , Neuroimaging/methods , Plaque, Amyloid/pathology , Positron-Emission Tomography , Alzheimer Disease/genetics , Alzheimer Disease/metabolism , Amyloid beta-Peptides/genetics , Amyloidosis/pathology , Animals , Animals, Genetically Modified , Biomarkers , Brain Chemistry , Cognitive Dysfunction/pathology , Female , Fluorine Radioisotopes , Fluorodeoxyglucose F18 , Male , Memory Disorders/metabolism , Mutation , Plaque, Amyloid/chemistry , Protein Aggregation, Pathological , Radiopharmaceuticals , Rats , Rats, Transgenic , Rats, WistarABSTRACT
Recently, a group of major international experts have completed a comprehensive effort to efficiently define a harmonized protocol for manual hippocampal segmentation that is optimized for Alzheimer's research (known as the EADC-ADNI Harmonized Protocol (the HarP)). This study compares the HarP with one of the widely used hippocampal segmentation protocols (Pruessner, 2000), based on a single automatic segmentation method trained separately with libraries made from each manual segmentation protocol. The automatic segmentation conformity with the corresponding manual segmentation and the ability to capture Alzheimer's disease related hippocampal atrophy on large datasets are measured to compare the manual protocols. In addition to the possibility of harmonizing different procedures of hippocampal segmentation, our results show that using the HarP, the automatic segmentation conformity with manual segmentation is also preserved (Dice's κ=0.88,κ=0.87 for Pruessner and HarP respectively (pâ¯=â¯0.726 for common training library)). Furthermore, the results show that the HarP can capture the Alzheimer's disease related hippocampal volume differences in large datasets. The HarP-derived segmentation shows large effect size (Cohen's d = 1.5883) in separating Alzheimer's Disease patients versus normal controls (AD:NC) and medium effect size (Cohen's d = 0.5747) in separating stable versus progressive Mild Cognitively Impaired patients (sMCI:pMCI). Furthermore, the area under the ROC curve for a LDA classifier trained based on age, sex and HarP-derived hippocampal volume is 0.8858 for AD:NC, and for 0.6677 sMCI:pMCI. These results show that the harmonized protocol-derived labels can be widely used in clinic and research, as a sensitive and accurate way of delineating the hippocampus.