Fully automated analysis using BRAINS: AutoWorkup

Posterior Fossa Sub-Arachnoid Cysts Observed in Patients with Bipolar Disorder: a Retrospective Cohort Study

Posterior fossa arachnoid cysts (PFACs) are rare congenital abnormalities observed in 0.3 to 1.7% of the population and are traditionally thought to be benign. While conducting a neuroimaging study investigating cerebellar structure in bipolar disorder, we observed a higher incidence of PFACs in bipolar patients (5 of 75; 6.6%) compared to the neuronormative control group (1 of 54; 1.8%). In this report, we detail the cases of the five patients with bipolar disorder who presented with PFACs. Additionally, we compare neuropsychiatric measures and cerebellar volumes of these patients to neuronormative controls and bipolar controls (those with bipolar disorder without neuroanatomical abnormalities). Our findings suggest that patients with bipolar disorder who also present with PFACs may have a milder symptom constellation relative to patients with bipolar disorder and no neuroanatomical abnormalities. Furthermore, our observations align with prior literature suggesting an association between PFACs and psychiatric symptoms that warrants further study. While acknowledging sample size limitations, our primary aim in the present work is to highlight a connection between PFACs and BD-associated symptoms and encourage further study of cerebellar abnormalities in psychiatry.

Associations between neurofilament light-chain protein, brain structure, and chronic kidney disease

BACKGROUND

Neurofilament light-chain (NfL) protein is a blood-based marker of neuroaxonal injury. We sought to (1) compare plasma NfL levels in children with chronic kidney disease (CKD) and healthy peers, (2) characterize the relationship between NfL level and kidney function, and (3) evaluate NfL as a predictor of abnormal brain structure in CKD.

METHODS

Sixteen children with CKD due to congenital kidney anomalies and 23 typically developing peers were included. Plasma NfL was quantified using single-molecule array immunoassay. Participants underwent structural magnetic resonance imaging. Multiple linear regression models were used to evaluate the association between plasma NfL levels, kidney function, and brain structure.

RESULTS

An age × group interaction was identified whereby NfL levels increased with age in the CKD group only (estimate = 0.65; confidence interval (CI) = 0.08-1.22; p = 0.026). Decreased kidney function was associated with higher NfL levels (estimate = -0.10; CI = -0.16 to -0.04; p = 0.003). Lower cerebellar gray matter volume predicted increased plasma NfL levels (estimate = -0.00024; CI = -0.00039 to 0.00009; p = 0.004) within the CKD group.

CONCLUSIONS

Children with CKD show accelerated age-related increases in NfL levels. NfL level is associated with lower kidney function and abnormal brain structure in CKD.

IMPACT

NfL is a component of the neuronal cytoskeleton providing structural axonal support. Elevated NfL has been described in relation to gray and white matter brain volume loss. We have previously described the abnormal cerebellar gray matter in CKD. We explored the relationship between NfL, CKD, and brain volume. There is an accelerated, age-related increase in NfL level in CKD. Within the CKD sample, NfL level is associated with abnormal kidney function and brain structure. Decreased kidney function may be linked to abnormal neuronal integrity in pediatric CKD.

Distinct patterns of altered quantitative T1ρ and functional BOLD response associated with history of suicide attempts in bipolar disorder

Despite the high risk for suicide, relatively few studies have explored the relationship between suicide and brain imaging measures in bipolar disorder. In addition, fewer studies have explored the possibility that altered brain metabolism may be associated with suicide attempt. To begin to fill in these gaps, we evaluated functional (task based fMRI) and metabolic (quantitative T1ρ) differences associated with suicide attempt in participants with bipolar disorder. Thirty-nine participants with bipolar disorder underwent fMRI during a flashing checkerboard task and 27 also underwent quantitative T1ρ. The relationship between neuroimaging and history of suicide attempt was tested using multiple regression while adjusting for age, sex, and current mood state. Differences between two measures of suicide attempt (binary: yes/no and continuous: number of attempts) were quantified using the corrected Akaike Information Criterion. Participants who had attempted suicide had greater fMRI task-related activation in visual areas and the cerebellum. The number of suicide attempts was associated with a difference in BOLD response in the amygdala, prefrontal cortex, and cerebellum. Increased quantitative T1ρ was associated with number of suicide attempts in limbic, basal ganglia, and prefrontal cortex regions. This study is a secondary analysis with a modest sample size. Differences between measures of suicide history may be due to differences in statistical power. History of suicide was associated with limbic, prefrontal, and cerebellar alterations. Results comparing those with and without suicide attempts differed from results using number of suicide attempts, suggesting that these variables have different neurobiological underpinnings.

Automated atlas-based segmentation for skull base surgical planning

PURPOSE

Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning.

METHODS

Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets.

RESULTS

The mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59-0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections.

CONCLUSION

Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259 ) with this paper will allow for general use of automated atlas-based segmentation of the skull base.

Brain structure and neural activity related to reading in boys with isolated oral clefts

Background: The purpose of this study was to evaluate brain structure and function in participants with iCL/P and unaffected controls. Effects of cleft presence and reading status (average vs impaired) were evaluated.Methods: Males, ages 8-11 years old, including 26 with iCL/P and 57 unaffected peers were recruited and coded for reading status (average vs impaired). All participants underwent a volumetric and task-based functional MRI. Volumes and significant regions of activation during the decoding task were obtained. Main effects of cleft and reading status, and their interaction were evaluated.Results: Participants with iCL/P had significantly increased frontal gray matter volume (associated with average reading) and occipital gray and white matter volume (associated with impaired reading). Impaired readers with iCL/P had a distinctive activation pattern in visual association and motor regions relative to other groups.Conclusions: Findings suggest that increases in frontal gray matter volume may be associated with effective compensation during reading, while posterior increases in occipital volume may be associated with ineffective compensation for participants with iCL/P. These patterns were different from idiopathic dyslexia. Further work in a larger sample is needed to determine if these differences are associated with cleft type and with sex.Abbreviations: iCL/P (isolated cleft lip and/or palate); iCL (isolated cleft lip only); iCLP (isolated cleft lip and palate); iCP (isolated cleft palate only); uAR (unaffected average reader); uIR (unaffected impaired reader); cAR (average reader with iCL/P); cIR (impaired reader with iCL/P).

MRI subcortical segmentation in neurodegeneration with cascaded 3D CNNs

The subcortical structures of the brain are relevant for many neurodegenerative diseases like Huntington's disease (HD). Quantitative segmentation of these structures from magnetic resonance images (MRIs) has been studied in clinical and neuroimaging research. Recently, convolutional neural networks (CNNs) have been successfully used for many medical image analysis tasks, including subcortical segmentation. In this work, we propose a 2-stage cascaded 3D subcortical segmentation framework, with the same 3D CNN architecture for both stages. Attention gates, residual blocks and output adding are used in our proposed 3D CNN. In the first stage, we apply our model to downsampled images to output a coarse segmentation. Next, we crop the extended subcortical region from the original image based on this coarse segmentation, and we input the cropped region to the second CNN to obtain the final segmentation. Left and right pairs of thalamus, caudate, pallidum and putamen are considered in our segmentation. We use the Dice coefficient as our metric and evaluate our method on two datasets: the publicly available IBSR dataset and a subset of the PREDICT-HD database, which includes healthy controls and HD subjects. We train our models on only healthy control subjects and test on both healthy controls and HD subjects to examine model generalizability. Compared with the state-of-the-art methods, our method has the highest mean Dice score on all considered subcortical structures (except the thalamus on IBSR), with more pronounced improvement for HD subjects. This suggests that our method may have better ability to segment MRIs of subjects with neurodegenerative disease.

Early pediatric chronic kidney disease is associated with brain volumetric gray matter abnormalities

BACKGROUND

The impact of pediatric chronic kidney disease (pCKD) on the brain remains poorly defined. The objective of this study was to compare brain morphometry between children with early-stage pCKD and typically developing peers using structural magnetic resonance imaging (MRI).

METHODS

The sample age range was 6-16 years. A total of 18 children with a diagnosis of pCKD (CKD stages 1-3) due to congenital anomalies of the kidney and urinary tract and 24 typically developing peers were included. Volumetric data from MRI and neurocognitive testing were compared using linear models including pCKD status, age, maternal education level, and socioeconomic status.

RESULTS

Cerebellar gray matter volume was significantly smaller in pCKD, t₍₃₈₎ = -2.71, p = 0.01. In contrast, cerebral gray matter volume was increased in pCKD, t₍₃₈₎ = 2.08, p = 0.04. Reduced cerebellum gray matter volume was associated with disease severity, operationalized as estimated glomerular filtration rate (eGFR), t₍₁₄₎ = 2.21, p = 0.04 and predicted lower verbal fluency scores in the pCKD sample. Enlarged cerebral gray matter in the pCKD sample predicted lower scores on mathematics assessment.

CONCLUSIONS

This study provides preliminary evidence for a morphometric underpinning to the cognitive deficits observed in pCKD.

IMPACT

The impact of pediatric chronic kidney disease (CKD) on the brain remains poorly defined, with no data linking brain morphometry and observed cognitive deficits noted in this population. We explored the relationship between brain morphometry (using structural magnetic resonance imaging), cognition, and markers of CKD. Cerebellar and cerebral gray matter volumes are different in early CKD. Volumetric decreases in cerebellar gray matter are predicted by lower eGFR, suggesting a link between disease and brain morphometry. Reduced cerebellar gray matter predicted lower verbal fluency for those with pCKD. Enlarged cerebral gray matter in the pCKD sample predicted lower mathematics performance.

Longitudinal subcortical segmentation with deep learning

Longitudinal information is important for monitoring the progression of neurodegenerative diseases, such as Huntington's disease (HD). Specifically, longitudinal magnetic resonance imaging (MRI) studies may allow the discovery of subtle intra-subject changes over time that may otherwise go undetected because of inter-subject variability. For HD patients, the primary imaging-based marker of disease progression is the atrophy of subcortical structures, mainly the caudate and putamen. To better understand the course of subcortical atrophy in HD and its correlation with clinical outcome measures, highly accurate segmentation is important. In recent years, subcortical segmentation methods have moved towards deep learning, given the state-of-the-art accuracy and computational efficiency provided by these models. However, these methods are not designed for longitudinal analysis, but rather treat each time point as an independent sample, discarding the longitudinal structure of the data. In this paper, we propose a deep learning based subcortical segmentation method that takes into account this longitudinal information. Our method takes a longitudinal pair of 3D MRIs as input, and jointly computes the corresponding segmentations. We use bi-directional convolutional long short-term memory (C-LSTM) blocks in our model to leverage the longitudinal information between scans. We test our method on the PREDICT-HD dataset and use the Dice coefficient, average surface distance and 95-percent Hausdorff distance as our evaluation metrics. Compared to cross-sectional segmentation, we improve the overall accuracy of segmentation, and our method has more consistent performance across time points. Furthermore, our method identifies a stronger correlation between subcortical volume loss and decline in the total motor score, an important clinical outcome measure for HD.

Brainstem and striatal volume changes are detectable in under 1 year and predict motor decline in spinocerebellar ataxia type 1

Spinocerebellar ataxia type 1 is a progressive neurodegenerative, movement disorder. With potential therapies on the horizon, it is critical to identify biomarkers that (i) differentiate between unaffected and spinocerebellar ataxia Type 1-affected individuals; (ii) track disease progression; and (iii) are directly related to clinical changes of the patient. Magnetic resonance imaging of volumetric changes in the brain may be a suitable source of biomarkers for spinocerebellar ataxia Type 1. In a previous report on a longitudinal study of patients with spinocerebellar ataxia Type 1, we evaluated the volume and magnetic resonance spectroscopy measures of the cerebellum and pons, showing pontine volume and pontine N-acetylaspartate-to-myo-inositol ratio were sensitive to change over time. As a follow-up, the current study conducts a whole brain exploration of volumetric MRI measures with the aim to identify biomarkers for spinocerebellar ataxia Type 1 progression. We adapted a joint label fusion approach using multiple, automatically generated, morphologically matched atlases to label brain regions including cerebellar sub-regions. We adjusted regional volumes by total intracranial volume allowing for linear and power-law relationships. We then utilized Bonferroni corrected linear mixed effects models to (i) determine group differences in regional brain volume and (ii) identify change within affected patients only. We then evaluated the rate of change within each brain region to identify areas that changed most rapidly. Lastly, we used a penalized, linear mixed effects model to determine the strongest brain predictors of motor outcomes. Decrease in pontine volume and accelerating decrease in putamen volume: (i) reliably differentiated spinocerebellar ataxia Type 1-affected and -unaffected individuals; (ii) were observable in affected individuals without referencing an unaffected comparison group; (iii) were detectable within ∼6–9 months; and (iv) were associated with increased disease burden. In conclusion, volumetric change in the pons and putamen may provide powerful biomarkers to track disease progression in spinocerebellar ataxia Type 1. The methods employed here are readily translatable to current clinical settings, providing a framework for study and usage of volumetric neuroimaging biomarkers for clinical trials.

Generalizing MRI Subcortical Segmentation to Neurodegeneration

Many neurodegenerative diseases like Huntington's disease (HD) affect the subcortical structures of the brain, especially the caudate and the putamen. Automated segmentation of subcortical structures from MRI scans is thus important in HD studies. LiviaNET [2] is the state-of-the-art deep learning approach for subcortical segmentation. As all learning-based models, this approach requires appropriate training data. While annotated healthy control images are relatively easy to obtain, generating such annotations for each new disease population can be prohibitively expensive. In this work, we explore LiviaNET variants using well-known strategies for improving performance, to make it more generalizable to patients with substantial neurodegeneration. Specifically, we explored Res-blocks in our convolutional neural network, and we also explored manipulating the input to the network as well as random elastic deformations for data augmentation. We tested our method on images from the PREDICT-HD dataset, which includes control and HD subjects. We trained on control subjects and tested on both controls and HD patients. Compared to the original LiviaNET, we improved the accuracy of most structures, both for controls and for HD patients. The caudate has the most pronounced improvement in HD subjects with the proposed modifications to LiviaNET, which is noteworthy since caudate is known to be severely atrophied in HD. This suggests our extensions may improve the generalization ability of LiviaNET to cohorts where significant neurodegeneration is present, without needing to be retrained.

Patch-Based Abnormality Maps for Improved Deep Learning-Based Classification of Huntington's Disease

Deep learning techniques have demonstrated state-of-the-art performances in many medical imaging applications. These methods can efficiently learn specific patterns. An alternative approach to deep learning is patch-based grading methods, which aim to detect local similarities and differences between groups of subjects. This latter approach usually requires less training data compared to deep learning techniques. In this work, we propose two major contributions: first, we combine patch-based and deep learning methods. Second, we propose to extend the patch-based grading method to a new patch-based abnormality metric. Our method enables us to detect localized structural abnormalities in a test image by comparison to a template library consisting of images from a variety of healthy controls. We evaluate our method by comparing classification performance using different sets of features and models. Our experiments show that our novel patch-based abnormality metric increases deep learning performance from 91.3% to 95.8% of accuracy compared to standard deep learning approaches based on the MRI intensity.

Subcortical T1-Rho MRI Abnormalities in Juvenile-Onset Huntington's Disease

Huntington’s disease (HD) is a fatal neurodegenerative disease caused by the expansion of cytosine-adenine-guanine (CAG) repeats in the huntingtin gene. An increased CAG repeat length is associated with an earlier disease onset. About 5% of HD cases occur under the age of 21 years, which are classified as juvenile-onset Huntington’s disease (JOHD). Our study aims to measure subcortical metabolic abnormalities in JOHD participants. T1-Rho (T_1ρ) MRI was used to compare brain regions of 13 JOHD participants and 39 controls. Region-of-interest analyses were used to assess differences in quantitative T_1ρ relaxation times. We found that the mean relaxation times in the caudate (p < 0.001), putamen (p < 0.001), globus pallidus (p < 0.001), and thalamus (p < 0.001) were increased in JOHD participants compared to controls. Furthermore, increased T_1ρ relaxation times in these areas were significantly associated with lower volumes amongst participants in the JOHD group. These findings suggest metabolic abnormalities in brain regions previously shown to degenerate in JOHD. We also analyzed the relationships between mean regional T_1ρ relaxation times and Universal Huntington’s Disease Rating Scale (UHDRS) scores. UHDRS was used to evaluate participants’ motor function, cognitive function, behavior, and functional capacity. Mean T_1ρ relaxation times in the caudate (p = 0.003), putamen (p = 0.005), globus pallidus (p = 0.009), and thalamus (p = 0.015) were directly proportional to the UHDRS score. This suggests that the T_1ρ relaxation time may also predict HD-related motor deficits. Our findings suggest that subcortical metabolic abnormalities drive the unique hypokinetic symptoms in JOHD.

TENSOR-BASED GRADING: A NOVEL PATCH-BASED GRADING APPROACH FOR THE ANALYSIS OF DEFORMATION FIELDS IN HUNTINGTON'S DISEASE

The improvements in magnetic resonance imaging have led to the development of numerous techniques to better detect structural alterations caused by neurodegenerative diseases. Among these, the patch-based grading framework has been proposed to model local patterns of anatomical changes. This approach is attractive because of its low computational cost and its competitive performance. Other studies have proposed to analyze the deformations of brain structures using tensor-based morphometry, which is a highly interpretable approach. In this work, we propose to combine the advantages of these two approaches by extending the patch-based grading framework with a new tensor-based grading method that enables us to model patterns of local deformation using a log-Euclidean metric. We evaluate our new method in a study of the putamen for the classification of patients with pre-manifest Huntington's disease and healthy controls. Our experiments show a substantial increase in classification accuracy (87.5 ± 0.5 vs. 81.3 ± 0.6) compared to the existing patch-based grading methods, and a good complement to putamen volume, which is a primary imaging-based marker for the study of Huntington's disease.

Brain Structural Features of Myotonic Dystrophy Type 1 and their Relationship with CTG Repeats

Background

Few adequately-powered studies have systematically evaluated brain morphology in adult-onset myotonic dystrophy type 1 (DM1).

Objective

The goal of the present study was to determine structural brain differences between individuals with and without adult-onset DM1 in a multi-site, case-controlled cohort. We also explored correlations between brain structure and CTG repeat length.

Methods

Neuroimaging data was acquired in 58 unaffected individuals (29 women) and 79 individuals with DM1 (50 women). CTG repeat length, expressed as estimated progenitor allele length (ePAL), was determined by small pool PCR. Statistical models were adjusted for age, sex, site, and intracranial volume (ICV).

Results

ICV was reduced in DM1 subjects compared with controls. Accounting for the difference in ICV, the DM1 group exhibited smaller volume in frontal grey and white matter, parietal grey matter as well as smaller volume of the corpus callosum, thalamus, putamen, and accumbens. In contrast, volumes of the hippocampus and amygdala were significantly larger in DM1. Greater ePAL was associated with lower volumes of the putamen, occipital grey matter, and thalamus. A positive ePAL association was observed for amygdala volume and cerebellar white matter.

Conclusions

Smaller ICV may be a marker of aberrant neurodevelopment in adult-onset DM1. Volumetric analysis revealed morphological differences, some associated with CTG repeat length, in structures with plausible links to key DM1 symptoms including cognitive deficits and excessive daytime somnolence. These data offer further insights into the basis of CNS disease in DM1, and highlight avenues for further work to identify therapeutic targets and imaging biomarkers.

Cognitive function and its relationship with brain structure in myotonic dystrophy type 1

Studies have shown relationships between white matter abnormalities and cognitive dysfunction in myotonic dystrophy type 1 (DM1), but comprehensive analysis of potential structure-function relationships are lacking. Fifty adult-onset DM1 individuals (33 female) and 68 unaffected adults (45 female) completed the Wechsler Adult Intelligence Scale-IV (WAIS-IV) to determine the levels and patterns of intellectual functioning. Neuroimages were acquired with a 3T scanner and were processed with BrainsTools. Regional brain volumes (regions of interest, ROIs) were adjusted for inter-scanner variation and intracranial volume. Linear regression models were conducted to assess if group by ROI interaction terms significantly predicted WAIS-IV composite scores. Models were adjusted for age and sex. The DM1 group had lower Perceptual Reasoning Index (PRI), Working Memory Index (WMI), and Processing Speed Index (PSI) scores than the unaffected group (PRI t₍₁₁₃₎  = -3.28, p = 0.0014; WMI t₍₁₁₄₎  = -3.49, p = 0.0007; PSI t₍₁₁₄₎  = -2.98, p = 0.0035). The group by hippocampus interaction term was significant for both PRI and PSI (PRI (t₍₁₁₁₎  = -2.82, p = 0.0057; PSI (t₍₁₁₂₎  = -2.87, p = 0.0049)). There was an inverse association between hippocampal volume and both PRI and PSI in the DM1 group (the higher the volume, the lower the intelligence quotient scores), but no such association was observed in the unaffected group. Enlarged hippocampal volume may underlie some aspects of cognitive dysfunction in adult-onset DM1, suggesting that increased volume of the hippocampus may be pathological.

Long-term outcome of brain structure in female preterm infants: possible associations of liberal versus restrictive red blood cell transfusions

BACKGROUND

Preterm infants who receive differential red blood cell (RBC) transfusions at birth may show brain structure differences across development, including abnormalities in white matter (WM) structure and organization. This study investigated long-term outcomes of brain structure in female infants born preterm, at an average age of 13 years old, who received red blood cell (RBC) transfusions in the neonatal period according to a liberal or restrictive approach. Results from this study will increase understanding of the effects of transfusion on the developing brain.

STUDY DESIGN AND METHODS

This follow-up study included female preterm infants who participated in a clinical trial and had been randomized at birth to either a liberal or restrictive hematocrit threshold. Brain structures were measured in childhood using structural magnetic resonance imaging (MRI) scans. Due to the low number of females in the restrictive transfusion group at follow-up, additional females were recruited for inclusion. Main outcome measures included cerebral and subcortical brain region volumes.

RESULTS

Total intracranial volume was significantly decreased in females who were randomized to higher average hematocrit levels at birth. Infants in the liberal transfusion group had proportionately smaller volumes in all measures of regional cerebral WM and subcortical brain volumes, reaching significance for temporal lobe WM and caudate volumes.

CONCLUSION

Female premature infants who received a liberal transfusion threshold at birth had decreased WM volumes, which suggests the potential long-term neurodevelopmental risks associated with liberal transfusion practices.

Brain structure in juvenile-onset Huntington disease

OBJECTIVE

To assess brain morphometry in a sample of patients with juvenile-onset Huntington disease (JOHD) and several mouse models of Huntington disease (HD) that likely represent the human JOHD phenotype.

METHODS

Despite sharing the mutation in the Huntingtin gene, adult-onset HD characteristically presents as a hyperkinetic motor disorder, while JOHD typically presents as a hypokinetic motor disease. The University of Iowa Kids-JHD program enrolls individuals 5 to 25 years of age who have already received the clinical diagnosis. A total of 19 children with juvenile HD (JHD) (mean CAG = 72) were studied. Patients with JHD were compared to healthy controls (n = 234) using a cross-sectional study design. Volumetric data from structural MRI was compared between groups. In addition, we used the same procedure to evaluate brain morphology of R6/2, zQ175, HdhQ250 HD mice models.

RESULTS

Participants with JHD had substantially reduced intracranial volumes. After controlling for the small intracranial volume size, the volumes of subcortical regions (caudate, putamen, globus pallidus, and thalamus) and of cortical white matter were significantly decreased in patients with JHD. However, the cerebellum was proportionately enlarged in the JHD sample. The cerebral cortex was largely unaffected. Likewise, HD mice had a lower volume of striatum and a higher volume of cerebellum, mirroring the human MRI results.

CONCLUSIONS

The primary pathology of JOHD extends beyond changes in the striatal volume. Brain morphology in both mice and human patients with JHD shows proportional cerebellar enlargement. This pattern of brain changes may explain the unique picture of hypokinetic motor symptoms in JHD, which is not seen in the hyperkinetic chorea-like phenotype of adult-onset HD.

Emerging differences between Huntington's disease-like 2 and Huntington's disease: A comparison using MRI brain volumetry

Huntington's Disease-Like 2 (HDL2), caused by a CTG/CAG expansion in JPH3 on chromosome 16q24, is the most common Huntington's Disease (HD) phenocopy in populations with African ancestry. Qualitatively, brain MRIs of HDL2 patients have been indistinguishable from HD. To determine brain regions most affected in HDL2 a cross-sectional study using MRI brain volumetry was undertaken to compare the brains of nine HDL2, 11 HD and nine age matched control participants. Participants were ascertained from the region in South Africa with the world's highest HDL2 incidence. The HDL2 and HD patient groups showed no significant differences with respect to mean age at MRI, disease duration, abnormal triplet repeat length, or age at disease onset. Overall, intracerebral volumes were smaller in both affected groups compared to the control group. Comparing the HDL2 and HD groups across multiple covariates, cortical and subcortical volumes were similar with the exception that the HDL2 thalamic volumes were smaller. Consistent with other similarities between the two diseases, these results indicate a pattern of neurodegeneration in HDL2 that is remarkably similar to HD. However smaller thalamic volumes in HDL2 raises intriguing questions into the pathogenesis of both disorders, and how these volumetric differences relate to their respective phenotypes.

Genetic Modification of Huntington Disease Acts Early in the Prediagnosis Phase

Age at onset of Huntington disease, an inherited neurodegenerative disorder, is influenced by the size of the disease-causing CAG trinucleotide repeat expansion in HTT and by genetic modifier loci on chromosomes 8 and 15. Stratifying by modifier genotype, we have examined putamen volume, total motor score (TMS), and symbol digit modalities test (SDMT) scores, both at study entry and longitudinally, in normal controls and CAG-expansion carriers who were enrolled prior to the emergence of manifest HD in the PREDICT-HD study. The modifiers, which included onset-hastening and onset-delaying alleles on chromosome 15 and an onset-hastening allele on chromosome 8, revealed no major effect in controls but distinct patterns of modification in prediagnosis HD subjects. Putamen volume at study entry showed evidence of reciprocal modification by the chromosome 15 alleles, but the rate of loss of putamen volume was modified only by the deleterious chromosome 15 allele. By contrast, both alleles modified the rate of change of the SDMT score, but neither had an effect on the TMS. The influence of the chromosome 8 modifier was evident only in the rate of TMS increase. The data indicate that (1) modification of pathogenesis can occur early in the prediagnosis phase, (2) the modifier loci act in genetic interaction with the HD mutation rather than through independent additive effects, and (3) HD subclinical phenotypes are differentially influenced by each modifier, implying distinct effects in different cells or tissues. Together, these findings indicate the potential benefit of using genetic modifier strategies for dissecting the prediagnosis pathogenic process in HD.

Alterations of the cerebellum and basal ganglia in bipolar disorder mood states detected by quantitative T1ρ mapping

OBJECTIVES

Quantitative mapping of T1 relaxation in the rotating frame (T1ρ) is a magnetic resonance imaging technique sensitive to pH and other cellular and microstructural factors, and is a potentially valuable tool for identifying brain alterations in bipolar disorder. Recently, this technique identified differences in the cerebellum and cerebral white matter of euthymic patients vs healthy controls that were consistent with reduced pH in these regions, suggesting an underlying metabolic abnormality. The current study built upon this prior work to investigate brain T1ρ differences across euthymic, depressed, and manic mood states of bipolar disorder.

METHODS

Forty participants with bipolar I disorder and 29 healthy control participants matched for age and gender were enrolled. Participants with bipolar disorder were imaged in one or more mood states, yielding 27, 12, and 13 imaging sessions in euthymic, depressed, and manic mood states, respectively. Three-dimensional, whole-brain anatomical images and T1ρ maps were acquired for all participants, enabling voxel-wise evaluation of T1ρ differences between bipolar mood state and healthy control groups.

RESULTS

All three mood state groups had increased T1ρ relaxation times in the cerebellum compared to the healthy control group. Additionally, the depressed and manic groups had reduced T1ρ relaxation times in and around the basal ganglia compared to the control and euthymic groups.

CONCLUSIONS

The study implicated the cerebellum and basal ganglia in the pathophysiology of bipolar disorder and its mood states, the roles of which are relatively unexplored. These findings motivate further investigation of the underlying cause of the abnormalities, and the potential role of altered metabolic activity in these regions.

Impaired sensory processing measured by functional MRI in Bipolar disorder manic and depressed mood states

Bipolar disorder is characterized by recurring episodes of depression and mania. Defining differences in brain function during these states is an important goal of bipolar disorder research. However, few imaging studies have directly compared brain activity between bipolar mood states. Herein, we compare functional magnetic resonance imaging (fMRI) responses during a flashing checkerboard stimulus between bipolar participants across mood states (euthymia, depression, and mania) in order to identify functional differences between these states. 40 participants with bipolar I disorder and 33 healthy controls underwent fMRI during the presentation of the stimulus. A total of 23 euthymic-state, 16 manic-state, 15 depressed-state, and 32 healthy control imaging sessions were analyzed in order to compare functional activation during the stimulus between mood states and with healthy controls. A reduced response was identified in the visual cortex in both the depressed and manic groups compared to euthymic and healthy participants. Functional differences between bipolar mood states were also observed in the cerebellum, thalamus, striatum, and hippocampus. Functional differences between mood states occurred in several brain regions involved in visual and other sensory processing. These differences suggest that altered visual processing may be a feature of mood states in bipolar disorder. The key limitations of this study are modest mood-state group size and the limited temporal resolution of fMRI which prevents the segregation of primary visual activity from regulatory feedback mechanisms.

Hippocampal atrophy, asymmetry, and cognition in type 2 diabetes mellitus

INTRODUCTION

Type 2 diabetes mellitus is associated with global and hippocampal atrophy and cognitive deficits, and some studies suggest that the right hippocampus may display greater vulnerability than the left.

METHODS

Hippocampal volumes, the hippocampal asymmetry index, and cognitive functioning were assessed in 120 nondemented adults with long duration type 2 diabetes.

RESULTS

The majority of the sample displayed left greater than right hippocampal asymmetry (which is the reverse of the expected direction seen with normal aging). After adjustment for age, sex, and IQ, right (but not left) hippocampal volumes were negatively associated with memory, executive function, and semantic fluency. These associations were stronger with the hippocampal asymmetry index and remained significant for memory and executive function after additional adjustment for global brain atrophy.

CONCLUSIONS

We conclude that asymmetric hippocampal atrophy may occur in type 2 diabetes, with greater atrophy occurring in the right than the left hippocampus, and that this may contribute to cognitive impairment in this disorder. These cross-sectional associations require further verification but may provide clues into the pathogenesis of cognitive disorders in type 2 diabetes.

Cortical Thickness in Adolescents with a Family History of Alcohol Use Disorder

BACKGROUND

Individuals with a family history (FH+) of alcohol use disorder (AUD) have a higher risk for developing an AUD than those with no family history (FH-) of AUD. In addition, FH+ individuals tend to perform worse on neuropsychological measures and show heightened impulsivity, which may be due to underlying differences in brain structure such as cortical thickness. The primary aim of this study was to investigate differences in cortical thickness in FH+ compared to FH- adolescents. Secondary aims were to (i) investigate differences in executive functioning and impulsivity, and (ii) examine associations between brain structure and behavior.

METHODS

Brain scans of 95 FH- and 93 FH+ subjects aged 13 to 18 were obtained using magnetic resonance imaging. FH+ subjects were required to have at least 1 biological parent with a history of an AUD. FH+ and FH- individuals had limited or no past alcohol use, thereby minimizing potential effects of alcohol. Subjects were evaluated on impulsivity and executive functioning tasks. Thicknesses of cortical lobes and subregions were analyzed using FreeSurfer. Regions showing group differences were examined for group-by-age interactions and correlations with neuropsychological and personality measures.

RESULTS

FH+ adolescents had thinner cortices in frontal and parietal lobes, notably in the medial orbitofrontal, lateral orbitofrontal, and superior parietal cortices. The difference in cortical thickness between family history groups was strongest among the youngest subjects. FH+ subjects were also more impulsive and had poorer performance on a spatial memory task.

CONCLUSIONS

These findings demonstrate frontal and parietal structural differences in FH+ adolescents that might underlie cognitive and behavioral characteristics associated with AUD risk.

Executive Functions in Healthy Older Adults Are Differentially Related to Macro- and Microstructural White Matter Characteristics of the Cerebral Lobes

Aging is associated with microstructural white matter (WM) changes. WM microstructural characteristics, measured with diffusion tensor imaging (DTI), are different in normal appearing white matter (NAWM) and WM hyperintensities (WMH). It is largely unknown how the microstructural properties of WMH are associated with cognition and if there are regional effects for specific cognitive domains. We therefore examined within 200 healthy older participants (a) differences in microstructural characteristics of NAWM and WMH per cerebral lobe; and (b) the association of macrostructural (WMH volume) and microstructural characteristics (within NAWM and WMH separately) of each lobe with measures of executive function and processing speed. Multi-modal imaging (i.e., T1, DTI, and FLAIR) was used to assess WM properties. The Stroop and the Trail Making Test were used to measure inhibition, task-switching (both components of executive function), and processing speed. We observed that age was associated with deterioration of white matter microstructure of the NAWM, most notably in the frontal lobe. Older participants had larger WMH volumes and lowest fractional anisotropy values within WMH were found in the frontal lobe. Task-switching was associated with cerebral NAWM volume and NAWM volume of all lobes. Processing speed was associated with total NAWM volume, and microstructural properties of parietal NAWM, the parietal WMH, and the temporal NAWM. Task-switching was related to microstructural properties of WMH of the frontal lobe and WMH volume of the parietal lobe. Our results confirm that executive functioning and processing speed are uniquely associated with macro- and microstructural properties of NAWM and WMH. We further demonstrate for the first time that these relationships differ by lobar region. This warrants the consideration of these distinct WM indices when investigating cognitive function.

Sex-specific effects of the Huntington gene on normal neurodevelopment

Huntington disease is a neurodegenerative disorder caused by a gene (HTT) with a unique feature of trinucleotide repeats ranging from 10 to 35 in healthy people; when expanded beyond 39 repeats, Huntington disease develops. Animal models demonstrate that HTT is vital to brain development; however, this has not been studied in humans. Moreover, evidence suggests that triplet repeat genes may have been vital in evolution of the human brain. Here we evaluate brain structure using magnetic resonance imaging and brain function using cognitive tests in a sample of school-aged children ages 6 to 18 years old. DNA samples were processed to quantify the number of CAG repeats within HTT. We find that the number of repeats in HTT, below disease threshold, confers advantageous changes in brain structure and general intelligence (IQ): the higher the number of repeats, the greater the change in brain structure, and the higher the IQ. The pattern of structural brain changes associated with HTT is strikingly different between males and females. HTT may confer an advantage or a disadvantage depending on the repeat length, playing a key role in either the evolution of a superior human brain or development of a uniquely human brain disease. © 2016 Wiley Periodicals, Inc.

Are Anesthesia and Surgery during Infancy Associated with Decreased White Matter Integrity and Volume during Childhood?

Background

Anesthetics have neurotoxic effects in neonatal animals. Relevant human evidence is limited. We sought such evidence in a structural neuroimaging study.

Methods

Two groups of children underwent structural magnetic resonance imaging: patients who, during infancy, had one of four operations commonly performed in otherwise healthy children and comparable, nonexposed control subjects. Total and regional brain tissue composition and volume, as well as regional indicators of white matter integrity (fractional anisotropy and mean diffusivity), were analyzed.

Results

Analyses included 17 patients, without potential confounding central nervous system problems or risk factors, who had general anesthesia and surgery during infancy and 17 control subjects (age ranges, 12.3 to 15.2 yr and 12.6 to 15.1 yr, respectively). Whole brain white matter volume, as a percentage of total intracranial volume, was lower for the exposed than the nonexposed group, 37.3 ± 0.4% and 38.9 ± 0.4% (least squares mean ± SE), respectively, a difference of 1.5 percentage points (95% CI, 0.3 to 2.8; P = 0.016). Corresponding decreases were statistically significant for parietal and occipital lobes, infratentorium, and brainstem separately. White matter integrity was lower for the exposed than the nonexposed group in superior cerebellar peduncle, cerebral peduncle, external capsule, cingulum (cingulate gyrus), and fornix (cres) and/or stria terminalis. The groups did not differ in total intracranial, gray matter, and cerebrospinal fluid volumes.

Conclusions

Children who had anesthesia and surgery during infancy showed broadly distributed, decreased white matter integrity and volume. Although the findings may be related to anesthesia and surgery during infancy, other explanations are possible.

Relationship altered between functional T1ρ and BOLD signals in bipolar disorder

INTRODUCTION

Functional neuroimaging typically relies on the blood-oxygen-level-dependent (BOLD) contrast, which is sensitive to the influx of oxygenated blood following neuronal activity. A new method, functional T1 relaxation in the rotating frame (fT1ρ) is thought to reflect changes in local brain metabolism, likely pH, and may more directly measure neuronal activity. These two methods were applied to study activation of the visual cortex in participants with bipolar disorder as compared to controls.

METHODS

Thirty-nine participants with bipolar disorder and 32 healthy controls underwent functional neuroimaging during a flashing checkerboard paradigm. Functional images were acquired in alternating blocks of BOLD and fT1ρ. Linear mixed-effect models were used to examine the relationship between these two functional imaging modalities and to test whether that relationship was altered in bipolar disorder.

RESULTS

BOLD and fT1ρ signal were strongly related in visual and cerebellar areas during the task in controls. The relationship between these two measures was reduced in bipolar disorder within the visual areas, cerebellum, striatum, and thalamus.

CONCLUSIONS

These results support a distinct mechanisms underlying BOLD and fT1ρ signals. The weakened relationship between these imaging modalities may provide a novel tool for measuring pathology in bipolar disorder and other psychiatric illnesses.

BIOMARKER CHANGE-POINT ESTIMATION WITH RIGHT CENSORING IN LONGITUDINAL STUDIES

We consider in this paper a statistical two-phase regression model in which the change point of a disease biomarker is measured relative to another point in time, such as the manifestation of the disease, which is subject to right-censoring (i.e., possibly unobserved over the entire course of the study). We develop point estimation methods for this model, based on maximum likelihood, and bootstrap validation methods. The effectiveness of our approach is illustrated by numerical simulations, and by the estimation of a change point for amygdalar atrophy in the context of Alzheimer's disease, wherein it is related to the cognitive manifestation of the disease.

Brain plasticity and sensorimotor deterioration as a function of 70 days head down tilt bed rest

Background

Adverse effects of spaceflight on sensorimotor function have been linked to altered somatosensory and vestibular inputs in the microgravity environment. Whether these spaceflight sequelae have a central nervous system component is unknown. However, experimental studies have shown spaceflight-induced brain structural changes in rodents’ sensorimotor brain regions. Understanding the neural correlates of spaceflight-related motor performance changes is important to ultimately develop tailored countermeasures that ensure mission success and astronauts’ health.

Method

Head down-tilt bed rest (HDBR) can serve as a microgravity analog because it mimics body unloading and headward fluid shifts of microgravity. We conducted a 70-day 6° HDBR study with 18 right-handed males to investigate how microgravity affects focal gray matter (GM) brain volume. MRI data were collected at 7 time points before, during and post-HDBR. Standing balance and functional mobility were measured pre and post-HDBR. The same metrics were obtained at 4 time points over ~90 days from 12 control subjects, serving as reference data.

Results

HDBR resulted in widespread increases GM in posterior parietal regions and decreases in frontal areas; recovery was not yet complete by 12 days post-HDBR. Additionally, HDBR led to balance and locomotor performance declines. Increases in a cluster comprising the precuneus, precentral and postcentral gyrus GM correlated with less deterioration or even improvement in standing balance. This association did not survive Bonferroni correction and should therefore be interpreted with caution. No brain or behavior changes were observed in control subjects.

Conclusions

Our results parallel the sensorimotor deficits that astronauts experience post-flight. The widespread GM changes could reflect fluid redistribution. Additionally, the association between focal GM increase and balance changes suggests that HDBR also may result in neuroplastic adaptation. Future studies are warranted to determine causality and underlying mechanisms.

Robust automated constellation-based landmark detection in human brain imaging

A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses the knowledge of each additional detected landmark as an improved foundation for improved prediction of the next landmark location. The accuracy and robustness of the presented method was evaluated by comparing the automatically generated results to two manual raters on 30 identified landmark points extracted from each of 30 T1-weighted magnetic resonance images. For the landmarks with unambiguous anatomical definitions, the average discrepancy between the algorithm results and each human observer differed by less than 1 mm from the average inter-observer variability when the algorithm was evaluated on imaging data collected from the same site as the model building data. Similar results were obtained when the same model was applied to a set of heterogeneous image volumes from seven different collection sites representing 3 scanner manufacturers. This method is reliable for general application in large-scale multi-site studies that consist of a variety of imaging data with different orientations, spacings, origins, and field strengths.

An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases

The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%.

Altered brain function, structure, and developmental trajectory in children born late preterm

BACKGROUND

Late preterm birth (34-36 wk gestation) is a common occurrence with potential for altered brain development.

METHODS

This observational cohort study compared children at age 6-13 y based on the presence or absence of the historical risk factor of late preterm birth. Children completed a battery of cognitive assessments and underwent magnetic resonance imaging of the brain.

RESULTS

Late preterm children (n = 52) demonstrated slower processing speed (P = 0.035) and scored more poorly in visual-spatial perception (P = 0.032) and memory (P = 0.007) than full-term children (n = 74). Parents of late preterm children reported more behavioral difficulty (P = 0.004). There were no group differences in cognitive ability or academic achievement. Imaging revealed similar intracranial volumes but less total tissue and more cerebrospinal fluid (P = 0.004) for late preterm children compared to full-term children. The tissue difference was driven by differences in the cerebrum (P = 0.028) and distributed across cortical (P = 0.051) and subcortical tissue (P = 0.047). Late preterm children had a relatively smaller thalamus (P = 0.012) than full-term children. Only full-term children demonstrated significant decreases in cortical tissue volume (P < 0.001) and thickness (P < 0.001) with age.

CONCLUSION

Late preterm birth may affect cognition, behavior, and brain structure well beyond infancy.

Linking white matter and deep gray matter alterations in premanifest Huntington disease

Huntington disease (HD) is a fatal progressive neurodegenerative disorder for which only symptomatic treatment is available. A better understanding of the pathology, and identification of biomarkers will facilitate the development of disease-modifying treatments. HD is potentially a good model of a neurodegenerative disease for development of biomarkers because it is an autosomal-dominant disease with complete penetrance, caused by a single gene mutation, in which the neurodegenerative process can be assessed many years before onset of signs and symptoms of manifest disease. Previous MRI studies have detected abnormalities in gray and white matter starting in premanifest stages. However, the understanding of how these abnormalities are related, both in time and space, is still incomplete. In this study, we combined deep gray matter shape diffeomorphometry and white matter DTI analysis in order to provide a better mapping of pathology in the deep gray matter and subcortical white matter in premanifest HD. We used 296 MRI scans from the PREDICT-HD database. Atrophy in the deep gray matter, thalamus, hippocampus, and nucleus accumbens was analyzed by surface based morphometry, and while white matter abnormalities were analyzed in (i) regions of interest surrounding these structures, using (ii) tractography-based analysis, and using (iii) whole brain atlas-based analysis. We detected atrophy in the deep gray matter, particularly in putamen, from early premanifest stages. The atrophy was greater both in extent and effect size in cases with longer exposure to the effects of the CAG expansion mutation (as assessed by greater CAP-scores), and preceded detectible abnormalities in the white matter. Near the predicted onset of manifest HD, the MD increase was widespread, with highest indices in the deep and posterior white matter. This type of in-vivo macroscopic mapping of HD brain abnormalities can potentially indicate when and where therapeutics could be targeted to delay the onset or slow the disease progression.

Dissociable contributions of amygdala and hippocampus to emotion and memory in patients with Alzheimer's disease

The amygdala and the hippocampus are associated with emotional processing and declarative memory, respectively. Studies have shown that patients with bilateral hippocampal damage caused by anoxia/ischemia, and patients with probable Alzheimer's disease (AD), can experience emotions for prolonged periods of time, even when they cannot remember what caused the emotion in the first place (Feinstein et al. (2010) Proc Natl Acad Sci USA 107:7674-7679; Guzmán-Vélez et al. (2014) Cogn Behav Neurol 27:117-129). This study aimed to investigate, for the first time, the roles of the amygdala and hippocampus in the dissociation between feelings of emotion and declarative memory for emotion-inducing events in patients with AD. Individuals with probable AD (N = 12) and age-matched healthy comparisons participants (HCP; N = 12) completed a high-resolution (0.44 × 0.44 × 0.80 mm) T2-weighted structural MR scan of the medial temporal lobe. Each of these individuals also completed two separate emotion induction procedures (sadness and happiness) using film clips. We collected real-time emotion ratings at baseline and multiple times postinduction, and administered a test of declarative memory shortly after each induction. Consistent with previous research, hippocampal volume was significantly smaller in patients with AD compared with HCP, and was positively correlated with memory for the film clips. Sustained feelings of emotion and amygdala volume did not significantly differ between patients with AD and HCP. Follow-up analyses showed a significant negative correlation between amygdala volume and sustained sadness, and a significant positive correlation between amygdala volume and sustained happiness. Our findings suggest that the amygdala is important for regulating and sustaining an emotion independent of hippocampal function and declarative memory for the emotion-inducing event. © 2015 Wiley Periodicals, Inc.

Peripheral inflammation during abnormal mood states in bipolar I disorder

BACKGROUND

Bipolar disorder carries a substantive morbidity and mortality burden, particularly related to cardiovascular disease. Abnormalities in peripheral inflammatory markers, which have been commonly reported in case-control studies, potentially link these co-morbidities. However, it is not clear whether inflammatory markers change episodically in response to mood states or are indicative of chronic pro-inflammatory activity, regardless of mood, in bipolar disorder.

METHODS

Investigations focused on comparing concentrations of specific inflammatory cytokines associated with immune activation status (primary outcome=tumor necrosis factor alpha (TNF-α)) in 37 participants with bipolar disorder across 3 mood states (mania N=15, depression N=9, normal mood N=13) and 29 controls without a psychiatric disorder (total N=66). Cytokine levels were also compared to T1ρ, a potential neuroimaging marker for inflammation, in select brain regions in a subsample (N=39).

RESULTS

Participants with bipolar disorder and healthy controls did not differ significantly in inflammatory cytokine concentrations. However, compared to cases with normal mood, cases with abnormal mood states (mania and depression) had significantly elevated levels of TNF-α, its soluble receptors (sTNFR1/sTNFR2), other macrophage-derived cytokines (interleukin 1β (IL-1β), IL-6, IL-10, and IL-18) in addition to IL-4, interferon-γ, monocyte chemotactic protein-1, fibroblast growth factor β, and vascular endothelial growth factor. Cytokine levels were not correlated with signals from T1ρ imaging in selected structures (amygdalae, hippocampi, hypothalamus, anterior cingulate gyrus, and middle frontal gyrus).

LIMITATIONS

Participants were not followed prospectively across mood states.

CONCLUSION

Activation of inflammatory markers was found in abnormal mood states of bipolar disorder. Longitudinal study of individuals with mood disorders is needed to confirm these findings and to elucidate the time course of any such changes.

Prefrontal cortex white matter tracts in prodromal Huntington disease

Huntington disease (HD) is most widely known for its selective degeneration of striatal neurons but there is also growing evidence for white matter (WM) deterioration. The primary objective of this research was to conduct a large-scale analysis using multisite diffusion-weighted imaging (DWI) tractography data to quantify diffusivity properties along major prefrontal cortex WM tracts in prodromal HD. Fifteen international sites participating in the PREDICT-HD study collected imaging and neuropsychological data on gene-positive HD participants without a clinical diagnosis (i.e., prodromal) and gene-negative control participants. The anatomical prefrontal WM tracts of the corpus callosum (PFCC), anterior thalamic radiations (ATRs), inferior fronto-occipital fasciculi (IFO), and uncinate fasciculi (UNC) were identified using streamline tractography of DWI. Within each of these tracts, tensor scalars for fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity coefficients were calculated. We divided prodromal HD subjects into three CAG-age product (CAP) groups having Low, Medium, or High probabilities of onset indexed by genetic exposure. We observed significant differences in WM properties for each of the four anatomical tracts for the High CAP group in comparison to controls. Additionally, the Medium CAP group presented differences in the ATR and IFO in comparison to controls. Furthermore, WM alterations in the PFCC, ATR, and IFO showed robust associations with neuropsychological measures of executive functioning. These results suggest long-range tracts essential for cross-region information transfer show early vulnerability in HD and may explain cognitive problems often present in the prodromal stage. Hum Brain Mapp 36:3717-3732, 2015. © 2015 Wiley Periodicals, Inc.

Multimodal neuroimaging computing: the workflows, methods, and platforms

The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.

Assessing atrophy measurement techniques in dementia: Results from the MIRIAD atrophy challenge

Structural MRI is widely used for investigating brain atrophy in many neurodegenerative disorders, with several research groups developing and publishing techniques to provide quantitative assessments of this longitudinal change. Often techniques are compared through computation of required sample size estimates for future clinical trials. However interpretation of such comparisons is rendered complex because, despite using the same publicly available cohorts, the various techniques have been assessed with different data exclusions and different statistical analysis models. We created the MIRIAD atrophy challenge in order to test various capabilities of atrophy measurement techniques. The data consisted of 69 subjects (46 Alzheimer's disease, 23 control) who were scanned multiple (up to twelve) times at nine visits over a follow-up period of one to two years, resulting in 708 total image sets. Nine participating groups from 6 countries completed the challenge by providing volumetric measurements of key structures (whole brain, lateral ventricle, left and right hippocampi) for each dataset and atrophy measurements of these structures for each time point pair (both forward and backward) of a given subject. From these results, we formally compared techniques using exactly the same dataset. First, we assessed the repeatability of each technique using rates obtained from short intervals where no measurable atrophy is expected. For those measures that provided direct measures of atrophy between pairs of images, we also assessed symmetry and transitivity. Then, we performed a statistical analysis in a consistent manner using linear mixed effect models. The models, one for repeated measures of volume made at multiple time-points and a second for repeated “direct” measures of change in brain volume, appropriately allowed for the correlation between measures made on the same subject and were shown to fit the data well. From these models, we obtained estimates of the distribution of atrophy rates in the Alzheimer's disease (AD) and control groups and of required sample sizes to detect a 25% treatment effect, in relation to healthy ageing, with 95% significance and 80% power over follow-up periods of 6, 12, and 24 months. Uncertainty in these estimates, and head-to-head comparisons between techniques, were carried out using the bootstrap. The lateral ventricles provided the most stable measurements, followed by the brain. The hippocampi had much more variability across participants, likely because of differences in segmentation protocol and less distinct boundaries. Most methods showed no indication of bias based on the short-term interval results, and direct measures provided good consistency in terms of symmetry and transitivity. The resulting annualized rates of change derived from the model ranged from, for whole brain: − 1.4% to − 2.2% (AD) and − 0.35% to − 0.67% (control), for ventricles: 4.6% to 10.2% (AD) and 1.2% to 3.4% (control), and for hippocampi: − 1.5% to − 7.0% (AD) and − 0.4% to − 1.4% (control). There were large and statistically significant differences in the sample size requirements between many of the techniques. The lowest sample sizes for each of these structures, for a trial with a 12 month follow-up period, were 242 (95% CI: 154 to 422) for whole brain, 168 (95% CI: 112 to 282) for ventricles, 190 (95% CI: 146 to 268) for left hippocampi, and 158 (95% CI: 116 to 228) for right hippocampi. This analysis represents one of the most extensive statistical comparisons of a large number of different atrophy measurement techniques from around the globe. The challenge data will remain online and publicly available so that other groups can assess their methods.

•

We compared numerous brain atrophy measurement techniques using multiple metrics.

•

Each participant produced measures on the exact same dataset, blinded to disease.

•

A central statistical analysis using linear mixed effect models was performed.

•

Head to head comparisons for each region were performed using sample size estimates.

•

Brain and ventricle measures were more consistent across groups than for hippocampi.

Larger hippocampus size in women with anorexia nervosa who exercise excessively than healthy women

Exercise has been shown to increase hippocampal volume in healthy older adults. Observations from animal models of diabetes and hypertension suggest that the combination of exercise and caloric restriction may exert greater neuroprotection in the hippocampus than either behavior alone. Yet, in humans, the effects of exercise and caloric restriction on the hippocampus are not known. We measured the volume of the hippocampus prior to clinical treatment in women with anorexia nervosa (AN) who were restricting calories and engaging in excessive exercise, women with AN who did not exercise excessively, and healthy women who did not engage in either behavior. Women with AN were also examined longitudinally (once weight was restored and 6 months later). In the present report, we found that women with AN engaged in caloric restriction and excessive exercising prior to clinical treatment had larger hippocampal volumes than healthy comparison women. After weight restoration, women with AN who had engaged in food restriction and excessive exercise prior to treatment had hippocampal volumes similar to that of women with AN who only engaged in caloric restriction. These results advance the field by showing for the first time that hippocampal volume may be increased by exercise alone or exercise interacting with food restriction in AN.

T1ρ imaging in premanifest Huntington disease reveals changes associated with disease progression

BACKGROUND

Imaging biomarkers sensitive to Huntington's disease (HD) during the premanifest phase preceding motor diagnosis may accelerate identification and evaluation of potential therapies. For this purpose, quantitative MRI sensitive to tissue microstructure and metabolism may hold great potential. We investigated the potential value of T1ρ relaxation to detect pathological changes in premanifest HD (preHD) relative to other quantitative relaxation parameters.

METHODS

Quantitative MR parametric mapping was used to assess differences between 50 preHD subjects and 26 age- and sex-matched controls. Subjects with preHD were classified into two progression groups based on their CAG-age product (CAP) score; a high and a low/moderate CAP group. Voxel-wise and region-of-interest analyses were used to assess changes in the quantitative relaxation times.

RESULTS

T1ρ showed a significant increase in the relaxation times in the high-CAP group, as compared to controls, largely in the striatum. The T1ρ changes in the preHD subjects showed a significant relationship with CAP score. No significant changes in T2 or T2* relaxation times were found in the striatum. T2* relaxation changes were found in the globus pallidus, but no significant changes with disease progression were found.

CONCLUSION

These data suggest that quantitative T1ρ mapping may provide a useful marker for assessing disease progression in HD. The absence of T2 changes suggests that the T1ρ abnormalities are unlikely owing to altered water content or tissue structure. The established sensitivity of T1ρ to pH and glucose suggests that these factors are altered in HD perhaps owing to abnormal mitochondrial function.

Brain abnormalities in bipolar disorder detected by quantitative T1ρ mapping

Abnormal metabolism has been reported in bipolar disorder, however, these studies have been limited to specific regions of the brain. To investigate whole-brain changes potentially associated with these processes, we applied a magnetic resonance imaging technique novel to psychiatric research, quantitative mapping of T1 relaxation in the rotating frame (T1ρ). This method is sensitive to proton chemical exchange, which is affected by pH, metabolite concentrations and cellular density with high spatial resolution relative to alternative techniques such as magnetic resonance spectroscopy and positron emission tomography. Study participants included 15 patients with bipolar I disorder in the euthymic state and 25 normal controls balanced for age and gender. T1ρ maps were generated and compared between the bipolar and control groups using voxel-wise and regional analyses. T1ρ values were found to be elevated in the cerebral white matter and cerebellum in the bipolar group. However, volumes of these areas were normal as measured by high-resolution T1- and T2-weighted magnetic resonance imaging. Interestingly, the cerebellar T1ρ abnormalities were normalized in participants receiving lithium treatment. These findings are consistent with metabolic or microstructural abnormalities in bipolar disorder and draw attention to roles of the cerebral white matter and cerebellum. This study highlights the potential utility of high-resolution T1ρ mapping in psychiatric research.

Regional atrophy associated with cognitive and motor function in prodromal Huntington disease

BACKGROUND

Neuroimaging studies suggest that volumetric MRI measures of specific brain structures may serve as excellent biomarkers in future clinical trials of Huntington disease (HD).

OBJECTIVE

Demonstration of the clinical significance of these measures is an important step in determining their appropriateness as potential outcome measures.

METHODS

Measures of gray- and white-matter lobular volumes and subcortical volumes (caudate, putamen, globus pallidus, thalamus, nucleus accumbens, hippocampus) were obtained from MRI scans of 516 individuals who tested positive for the HD gene expansion, but were not yet exhibiting signs or symptoms severe enough to warrant diagnosis ("pre-HD"). MRI volumes (corrected for intracranial volume) were correlated with cognitive, motor, psychiatric, and functional measures known to be sensitive to subtle changes in pre-HD.

RESULTS

Caudate, putamen, and globus pallidus volumes consistently correlated with cognitive and motor, but not psychiatric or functional measures in pre-HD. Volumes of white matter, nucleus accumbens, and thalamus, but not cortical gray matter, also correlated with some of the motor and cognitive measures.

CONCLUSIONS

Results of regression analyses suggest that volumes of basal ganglia structures contributed more highly to the prediction of most motor and cognitive variables than volumes of other brain regions. These results support the use of volumetric measures, especially of the basal ganglia, as outcome measures in future clinical trials in pre-HD. Results may also assist investigators in selecting the most appropriate measures for treatment trials that target specific clinical features or regions of neuropathology.

Application of tolerance limits to the characterization of image registration performance

Deformable image registration is used increasingly in image-guided interventions and other applications. However, validation and characterization of registration performance remain areas that require further study. We propose an analysis methodology for deriving tolerance limits on the initial conditions for deformable registration that reliably lead to a successful registration. This approach results in a concise summary of the probability of registration failure, while accounting for the variability in the test data. The (β, γ) tolerance limit can be interpreted as a value of the input parameter that leads to successful registration outcome in at least 100β% of cases with the 100γ% confidence. The utility of the methodology is illustrated by summarizing the performance of a deformable registration algorithm evaluated in three different experimental setups of increasing complexity. Our examples are based on clinical data collected during MRI-guided prostate biopsy registered using publicly available deformable registration tool. The results indicate that the proposed methodology can be used to generate concise graphical summaries of the experiments, as well as a probabilistic estimate of the registration outcome for a future sample. Its use may facilitate improved objective assessment, comparison and retrospective stress-testing of deformable.

The relationship between brain structure and cognition in transfused preterm children at school age

Examine the relationship between brain structure and cognition in preterm children randomly assigned to a liberal red blood cell (RBC) transfusion strategy as neonates. Intelligence, achievement, and neuropsychological measures were assessed and structural imaging was obtained (n = 26; 38% male). Global brain volumes were related to cognitive outcome. Additionally, females performed lower on verbal fluency; lower performance was related to temporal white matter volume. Findings provide possible evidence of the adverse effect of a liberal RBC transfusion strategy in which females had decreased temporal lobe white matter directly related to poor verbal fluency.

The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA's first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Stable Atlas-based Mapped Prior (STAMP) machine-learning segmentation for multicenter large-scale MRI data

Machine learning (ML)-based segmentation methods are a common technique in the medical image processing field. In spite of numerous research groups that have investigated ML-based segmentation frameworks, there remains unanswered aspects of performance variability for the choice of two key components: ML algorithm and intensity normalization. This investigation reveals that the choice of those elements plays a major part in determining segmentation accuracy and generalizability. The approach we have used in this study aims to evaluate relative benefits of the two elements within a subcortical MRI segmentation framework. Experiments were conducted to contrast eight machine-learning algorithm configurations and 11 normalization strategies for our brain MR segmentation framework. For the intensity normalization, a Stable Atlas-based Mapped Prior (STAMP) was utilized to take better account of contrast along boundaries of structures. Comparing eight machine learning algorithms on down-sampled segmentation MR data, it was obvious that a significant improvement was obtained using ensemble-based ML algorithms (i.e., random forest) or ANN algorithms. Further investigation between these two algorithms also revealed that the random forest results provided exceptionally good agreement with manual delineations by experts. Additional experiments showed that the effect of STAMP-based intensity normalization also improved the robustness of segmentation for multicenter data sets. The constructed framework obtained good multicenter reliability and was successfully applied on a large multicenter MR data set (n>3000). Less than 10% of automated segmentations were recommended for minimal expert intervention. These results demonstrate the feasibility of using the ML-based segmentation tools for processing large amount of multicenter MR images. We demonstrated dramatically different result profiles in segmentation accuracy according to the choice of ML algorithm and intensity normalization chosen.

Test-Retest Reliability of Diffusion Tensor Imaging in Huntington's Disease

Diffusion tensor imaging (DTI) has shown microstructural abnormalities in patients with Huntington's Disease (HD) and work is underway to characterise how these abnormalities change with disease progression. Using methods that will be applied in longitudinal research, we sought to establish the reliability of DTI in early HD patients and controls. Test-retest reliability, quantified using the intraclass correlation coefficient (ICC), was assessed using region-of-interest (ROI)-based white matter atlas and voxelwise approaches on repeat scan data from 22 participants (10 early HD, 12 controls). T1 data was used to generate further ROIs for analysis in a reduced sample of 18 participants. The results suggest that fractional anisotropy (FA) and other diffusivity metrics are generally highly reliable, with ICCs indicating considerably lower within-subject compared to between-subject variability in both HD patients and controls. Where ICC was low, particularly for the diffusivity measures in the caudate and putamen, this was partly influenced by outliers. The analysis suggests that the specific DTI methods used here are appropriate for cross-sectional research in HD, and give confidence that they can also be applied longitudinally, although this requires further investigation. An important caveat for DTI studies is that test-retest reliability may not be evenly distributed throughout the brain whereby highly anisotropic white matter regions tended to show lower relative within-subject variability than other white or grey matter regions.

Landmarking the brain for geometric morphometric analysis: an error study

Neuroanatomic phenotypes are often assessed using volumetric analysis. Although powerful and versatile, this approach is limited in that it is unable to quantify changes in shape, to describe how regions are interrelated, or to determine whether changes in size are global or local. Statistical shape analysis using coordinate data from biologically relevant landmarks is the preferred method for testing these aspects of phenotype. To date, approximately fifty landmarks have been used to study brain shape. Of the studies that have used landmark-based statistical shape analysis of the brain, most have not published protocols for landmark identification or the results of reliability studies on these landmarks. The primary aims of this study were two-fold: (1) to collaboratively develop detailed data collection protocols for a set of brain landmarks, and (2) to complete an intra- and inter-observer validation study of the set of landmarks. Detailed protocols were developed for 29 cortical and subcortical landmarks using a sample of 10 boys aged 12 years old. Average intra-observer error for the final set of landmarks was 1.9 mm with a range of 0.72 mm-5.6 mm. Average inter-observer error was 1.1 mm with a range of 0.40 mm-3.4 mm. This study successfully establishes landmark protocols with a minimal level of error that can be used by other researchers in the assessment of neuroanatomic phenotypes.

Neuroanatomical correlates of cognitive functioning in prodromal Huntington disease

INTRODUCTION

The brain mechanisms of cognitive impairment in prodromal Huntington disease (prHD) are not well understood. Although striatal atrophy correlates with some cognitive abilities, few studies of prHD have investigated whether cortical gray matter morphometry correlates in a regionally specific manner with functioning in different cognitive domains. This knowledge would inform the selection of cognitive measures for clinical trials that would be most sensitive to the target of a treatment intervention.

METHOD

In this study, random forest analysis was used to identify neuroanatomical correlates of functioning in five cognitive domains including attention and information processing speed, working memory, verbal learning and memory, negative emotion recognition, and temporal processing. Participants included 325 prHD individuals with varying levels of disease progression and 119 gene-negative controls with a family history of HD. In intermediate analyses, we identified brain regions that showed significant differences between the prHD and the control groups in cortical thickness and striatal volume. Brain morphometry in these regions was then correlated with cognitive functioning in each of the domains in the prHD group using random forest methods. We hypothesized that different regional patterns of brain morphometry would be associated with performances in distinct cognitive domains.

RESULTS

The results showed that performances in different cognitive domains that are vulnerable to decline in prHD were correlated with regionally specific patterns of cortical and striatal morphometry. Putamen and/or caudate volumes were top-ranked correlates of performance across all cognitive domains, as was cortical thickness in regions related to the processing demands of each domain.

CONCLUSIONS

The results underscore the importance of identifying structural magnetic resonance imaging (sMRI) markers of functioning in different cognitive domains, as their relative sensitivity depends on the extent to which processing is called upon by different brain networks. The findings have implications for identifying neuroimaging and cognitive outcome measures for use in clinical trials.

Individual differences in transcranial electrical stimulation current density

Transcranial electrical stimulation (TCES) is effective in treating many conditions, but it has not been possible to accurately forecast current density within the complex anatomy of a given subject's head. We sought to predict and verify TCES current densities and determine the variability of these current distributions in patient-specific models based on magnetic resonance imaging (MRI) data. Two experiments were performed. The first experiment estimated conductivity from MRIs and compared the current density results against actual measurements from the scalp surface of 3 subjects. In the second experiment, virtual electrodes were placed on the scalps of 18 subjects to model simulated current densities with 2 mA of virtually applied stimulation. This procedure was repeated for 4 electrode locations. Current densities were then calculated for 75 brain regions. Comparison of modeled and measured external current in experiment 1 yielded a correlation of r = .93. In experiment 2, modeled individual differences were greatest near the electrodes (ten-fold differences were common), but simulated current was found in all regions of the brain. Sites that were distant from the electrodes (e.g. hypothalamus) typically showed two-fold individual differences. MRI-based modeling can effectively predict current densities in individual brains. Significant variation occurs between subjects with the same applied electrode configuration. Individualized MRI-based modeling should be considered in place of the 10-20 system when accurate TCES is needed.