Voxel-based morphometry at ultra-high fields. a comparison of 7T and 3T MRI data

Leveraging ultra-high field (7T) MRI in psychiatric research

Non-invasive brain imaging has played a critical role in establishing our understanding of the neural properties that contribute to the emergence of psychiatric disorders. However, characterizing core neurobiological mechanisms of psychiatric symptomatology requires greater structural, functional, and neurochemical specificity than is typically obtainable with standard field strength MRI acquisitions (e.g., 3T). Ultra-high field (UHF) imaging at 7 Tesla (7T) provides the opportunity to identify neurobiological systems that confer risk, determine etiology, and characterize disease progression and treatment outcomes of major mental illnesses. Increases in scanner availability, regulatory approval, and sequence availability have made the application of UHF to clinical cohorts more feasible than ever before, yet the application of UHF approaches to the study of mental health remains nascent. In this technical review, we describe core neuroimaging methodologies which benefit from UHF acquisition, including high resolution structural and functional imaging, single (1H) and multi-nuclear (e.g., 31P) MR spectroscopy, and quantitative MR techniques for assessing brain tissue iron and myelin. We discuss advantages provided by 7T MRI, including higher signal- and contrast-to-noise ratio, enhanced spatial resolution, increased test-retest reliability, and molecular and neurochemical specificity, and how these have begun to uncover mechanisms of psychiatric disorders. Finally, we consider current limitations of UHF in its application to clinical cohorts, and point to ongoing work that aims to overcome technical hurdles through the continued development of UHF hardware, software, and protocols.

Neuroimaging at 7 Tesla: a pictorial narrative review

Neuroimaging using the 7-Tesla (7T) human magnetic resonance (MR) system is rapidly gaining popularity after being approved for clinical use in the European Union and the USA. This trend is the same for functional MR imaging (MRI). The primary advantages of 7T over lower magnetic fields are its higher signal-to-noise and contrast-to-noise ratios, which provide high-resolution acquisitions and better contrast, making it easier to detect lesions and structural changes in brain disorders. Another advantage is the capability to measure a greater number of neurochemicals by virtue of the increased spectral resolution. Many structural and functional studies using 7T have been conducted to visualize details in the white matter and layers of the cortex and hippocampus, the subnucleus or regions of the putamen, the globus pallidus, thalamus and substantia nigra, and in small structures, such as the subthalamic nucleus, habenula, perforating arteries, and the perivascular space, that are difficult to observe at lower magnetic field strengths. The target disorders for 7T neuroimaging range from tumoral diseases to vascular, neurodegenerative, and psychiatric disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, epilepsy, major depressive disorder, and schizophrenia. MR spectroscopy has also been used for research because of its increased chemical shift that separates overlapping peaks and resolves neurochemicals more effectively at 7T than a lower magnetic field. This paper presents a narrative review of these topics and an illustrative presentation of images obtained at 7T. We expect 7T neuroimaging to provide a new imaging biomarker of various brain disorders.

Gray Matter Volume Variability in Young Healthy Adults: Influence of Gender Difference and Brain-Derived Neurotrophic Factor Genotype

Although brain gray matter (GM) plastically changes during short-term training, it is still unclear whether brain structures are stable for short periods (several months). Therefore, this study aimed to re-test the short-term variability of GM volumes and to clarify the effect of factors (gender and BDNF-genotype) expected to contribute to such variability. The subjects comprised 41 young healthy adults. T1-weighted images were acquired twice with an interval of approximately 4 months using a 3 T-MRI scanner. Voxel-based morphometry (VBM) was used to calculate GM volumes in 47 regions. The intraclass correlation coefficient (ICC) and Test–retest variability (%TRV) were used as indices of variability. As a result, the ICCs in 43 regions were excellent (ICC > 0.90) and those in 3 regions were good (ICC > 0.80), whereas the ICC in the thalamus was moderate (ICC = 0.694). Women had a higher %TRV than men in 5 regions, and %TRV of the Val66Val group was higher than that of the Met carrier group in 2 regions. Moreover, the Female-Val66Val group had a higher %TRV than the Male-Met carrier group in 3 regions. These results indicate that although the short-term variability of GM volumes is small, it is affected by within-subject factors.

High-resolution Structural Magnetic Resonance Imaging and Quantitative Susceptibility Mapping

High-resolution 7-T imaging and quantitative susceptibility mapping produce greater anatomic detail compared with conventional strengths because of improvements in signal/noise ratio and contrast. The exquisite anatomic details of deep structures, including delineation of microscopic architecture using advanced techniques such as quantitative susceptibility mapping, allows improved detection of abnormal findings thought to be imperceptible on clinical strengths. This article reviews caveats and techniques for translating sequences commonly used on 1.5 or 3 T to high-resolution 7-T imaging. It discusses for several broad disease categories how high-resolution 7-T imaging can advance the understanding of various diseases, improve diagnosis, and guide management.

Can 7T MPRAGE match MP2RAGE for gray-white matter contrast?

Ultra-High Field (UHF) MRI provides a significant increase in Signal-to-Noise Ratio (SNR) and gains in contrast weighting in several functional and structural acquisitions. Unfortunately, an increase in field strength also induces non-uniformities in the transmit field (B1+) that can compromise image contrast non-uniformly. The MPRAGE is one of the most common T1 weighted (T1w) image acquisitions for structural imaging. It provides excellent contrast between gray and white matter and is widely used for brain segmentation. At 7T, the signal non-uniformities tend to complicate this and therefore, the self-bias-field corrected MP2RAGE is often used there. In both MPRAGE and MP2RAGE, more homogeneous image contrast can be achieved with adiabatic pulses, like the TR-FOCI inversion pulse, or special pulse design on parallel transmission systems, like Universal Pulses (UP). In the present study, we investigate different strategies to improve the bias-field for MPRAGE at 7T, comparing the contrast and GM/WM segmentability against MP2RAGE. The higher temporal efficiency of MPRAGE combined with the potential of the user-friendly UPs was the primary motivation for this MPRAGE-MP2RAGE comparison. We acquired MPRAGE data in six volunteers, adding a k-space shutter to reduce scan time, a kt-point UP approach for homogeneous signal excitation, and a TR-FOCI pulse for homogeneous inversion. Our results show remarkable signal contrast improvement throughout the brain, including regions of low B1⁺ such as the cerebellum. The improvements in the MPRAGE were largest following the introduction of the UPs. In addition to the CNR, both SNR and GM/WM segmentability were also assessed. Among the MPRAGEs, the combined strategy (UP + TR-FOCI) yielded highest SNR and showed highest spatial similarity between GM segments to the MP2RAGE. Interestingly, the distance between gray and white matter peaks in the intensity histograms did not increase, as better pulses and higher SNR especially benefitted the (cerebellar) gray matter. Overall, the gray-white matter contrast from MP2RAGE is higher, with higher CNR and higher intensity peak distances, even when scaled to scan time. Hence, the extra acquisition time for MP2RAGE is justified by the improved segmentability.

Measurement of Cortical Atrophy and Its Correlation to Memory Impairment in Patients With Asymptomatic Carotid Artery Stenosis Based on VBM-DARTEL

Objective

Severe carotid artery stenosis (CAS) can lead to atrophy of gray matter (GM) and memory impairment; however, the underlying mechanism is unknown. Thus, we aimed to identify memory impairment and GM atrophy and explore the possible correlation between them in patients with asymptomatic severe CAS.

Methods

Twenty-four patients with asymptomatic severe CAS and 10 healthy controls completed the mini-mental state examination (MMSE) and clinical memory scale (CMS) and underwent 7T magnetic resonance imaging (MRI) scan. Field intensity inhomogeneities were corrected. Images were processed using VBM8, and GM images were flipped. First, 11 flipped and 10 non-flipped images of patients with unilateral CAS and 5 flipped and 5 non-flipped images of controls were pre-processed using DARTEL algorithm and analyzed using an analysis of variance (ANOVA). Second, flipped and non-flipped images of unilateral patients were similarly pre-processed and analyzed using the paired t-test. Third, pre-processed non-flipped GM images and CMS scores of 24 patients were analyzed by multiple regression analysis. Nuisance variables were corrected accordingly.

Results

Basic information was well matched between patients and controls. MMSE scores of patients were in the normal range; however, memory function was significantly reduced (all P < 0.05). GM volumes of patients were significantly reduced in the anterior circulation regions. The stenosis-side hemispheres showed greater atrophy. GM volumes of the left pars opercularis, pars triangularis, and middle frontal gyrus were strongly positively correlated with the total scores of CMS (all r > 0.7, P = 0.001). Additionally, the left middle frontal gyrus was strongly positively correlated with associative memory (r = 0.853, P = 0.001). The left pars opercularis was moderately positively correlated with semantic memory (r = 0.695, P = 0.001).

Conclusion

Patients with asymptomatic CAS suffer from memory impairment. Bilateral anterior circulation regions showed extensive atrophy. The hemisphere with stenosis showed severer atrophy. Memory impairment in patients may be related to atrophy of the left frontal gyrus and atrophy of different regions may result in different memory impairments.

Sex Matters: A Multivariate Pattern Analysis of Sex- and Gender-Related Neuroanatomical Differences in Cis- and Transgender Individuals Using Structural Magnetic Resonance Imaging

Univariate analyses of structural neuroimaging data have produced heterogeneous results regarding anatomical sex- and gender-related differences. The current study aimed at delineating and cross-validating brain volumetric surrogates of sex and gender by comparing the structural magnetic resonance imaging data of cis- and transgender subjects using multivariate pattern analysis. Gray matter (GM) tissue maps of 29 transgender men, 23 transgender women, 35 cisgender women, and 34 cisgender men were created using voxel-based morphometry and analyzed using support vector classification. Generalizability of the models was estimated using repeated nested cross-validation. For external validation, significant models were applied to hormone-treated transgender subjects (n = 32) and individuals diagnosed with depression (n = 27). Sex was identified with a balanced accuracy (BAC) of 82.6% (false discovery rate [pFDR] < 0.001) in cisgender, but only with 67.5% (pFDR = 0.04) in transgender participants indicating differences in the neuroanatomical patterns associated with sex in transgender despite the major effect of sex on GM volume irrespective of the self-identification as a woman or man. Gender identity and gender incongruence could not be reliably identified (all pFDR > 0.05). The neuroanatomical signature of sex in cisgender did not interact with depressive features (BAC = 74.7%) but was affected by hormone therapy when applied in transgender women (P < 0.001).

Assessing Age-Related Gray Matter Differences in Young Adults with Voxel-Based Morphometry: The Effect of Field Strengths

Knowing the patterns of brain differences with age in the young population could lead to a better understanding of the causes of certain psychiatric disorders; however, relevant information is insufficient. Here, a pattern of regional gray matter (GM) that changed with age in a young cohort aged 20-30 years was provided. Extending from previous age studies, all participants were imaged at both 1.5 T and 3 T to address the question of how far the field strength influences results. Fifty-nine young participants aged 20-30 years were scanned at both 1.5 T and 3 T. Voxel-based morphometry (VBM) was used to estimate the GM volume. Some brain regions showed a significant field strength-dependent difference in GM volume. VBM uncovered a significantly age-related increase in the GM volume in the left visual-associated area at 3 T, which was not detected at 1.5 T. In addition, voxels at 1.5 T that revealed a significant age-related reduction in the GM volume were found in the right cerebellum. In conclusion, age-related differences in human brain morphology could even be detected in a young cohort aged 20-30 years; however, the results varied across field strengths. Thus, field strength should be considered an important factor when comparing age-specific brain differences across studies.

High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep CNN to label the hippocampus and amygdala on whole brain 700 μm isotropic 3D MP2RAGE MRI acquired at 7T. Manual labels of the hippocampus and amygdala were used to (i) train the predictive model and (ii) evaluate performance of the model when applied to new scans. Healthy controls and individuals with epilepsy were included in our analyses. Twenty-one healthy controls and sixteen individuals with epilepsy were included in the study. We utilized the recently developed DeepMedic software to train a CNN to label the hippocampus and amygdala based on manual labels. Performance was evaluated by measuring the dice similarity coefficient (DSC) between CNN-based and manual labels. A leave-one-out cross validation scheme was used. CNN-based and manual volume estimates were compared for the left and right hippocampus and amygdala in healthy controls and epilepsy cases. The CNN-based technique successfully labeled the hippocampus and amygdala in all cases. Mean DSC = 0.88 ± 0.03 for the hippocampus and 0.8 ± 0.06 for the amygdala. CNN-based labeling was independent of epilepsy diagnosis in our sample (p = .91). CNN-based volume estimates were highly correlated with manual volume estimates in epilepsy cases and controls. CNNs can label the hippocampus and amygdala on native sub-mm resolution MP2RAGE 7T MRI. Our findings suggest deep learning techniques can advance development of morphometric analysis techniques for high field strength, high spatial resolution brain MRI.

Neuroanatomical Changes in Leber's Hereditary Optic Neuropathy: Clinical Application of 7T MRI Submillimeter Morphometry

Leber’s hereditary optic neuropathy (LHON) is one of the mitochondrial diseases that causes loss of central vision, progressive impairment and subsequent degeneration of retinal ganglion cells (RGCs). In recent years, diffusion tensor imaging (DTI) studies have revealed structural abnormalities in visual white matter tracts, such as the optic tract, and optic radiation. However, it is still unclear if the disease alters only some parts of the white matter architecture or whether the changes also affect other subcortical areas of the brain. This study aimed to improve our understanding of morphometric changes in subcortical brain areas and their associations with the clinical picture in LHON by the application of a submillimeter surface-based analysis approach to the ultra-high-field 7T magnetic resonance imaging data. To meet these goals, fifteen LHON patients and fifteen age-matched healthy subjects were examined. For all individuals, quantitative analysis of the morphometric results was performed. Furthermore, morphometric characteristics which differentiated the groups were correlated with variables covering selected aspects of the LHON clinical picture. Compared to healthy controls (HC), LHON carriers showed significantly lower volume of both palladiums (left p = 0.023; right p = 0.018), the right accumbens area (p = 0.007) and the optic chiasm (p = 0.014). Additionally, LHON patients have significantly higher volume of both lateral ventricles (left p = 0.034; right p = 0.02), both temporal horns of the lateral ventricles (left p = 0.016; right p = 0.034), 3rd ventricle (p = 0.012) and 4th ventricle (p = 0.002). Correlation between volumetric results and clinical data showed that volume of both right and left lateral ventricles significantly and positively correlated with the duration of the illness (left R = 0.841, p = 0.002; right R = 0.755, p = 0.001) and the age of the LHON participants (left R = 0.656, p = 0.007; right R = 0.691, p = 0.004). The abnormalities in volume of the LHON patients’ subcortical structures indicate that the disease can cause changes not only in the white matter areas constituting visual tracts, but also in the other subcortical brain structures. Furthermore, the correlation between those results and the illness duration suggests that the disease might have a neurodegenerative nature; however, to fully confirm this observation, longitudinal studies should be conducted.

The Role of the Striatum in Learning to Orthogonalize Action and Valence: A Combined PET and 7 T MRI Aging Study

Pavlovian biases influence instrumental learning by coupling reward seeking with action invigoration and punishment avoidance with action suppression. Using a probabilistic go/no-go task designed to orthogonalize action (go/no-go) and valence (reward/punishment), recent studies have shown that the interaction between the two is dependent on the striatum and its key neuromodulator dopamine. Using this task, we sought to identify how structural and neuromodulatory age-related differences in the striatum may influence Pavlovian biases and instrumental learning in 25 young and 31 older adults. Computational modeling revealed a significant age-related reduction in reward and punishment sensitivity and marked (albeit not significant) reduction in learning rate and lapse rate (irreducible noise). Voxel-based morphometry analysis using 7 Tesla MRI images showed that individual differences in learning rate in older adults were related to the volume of the caudate nucleus. In contrast, dopamine synthesis capacity in the dorsal striatum, assessed using [18F]-DOPA positron emission tomography in 22 of these older adults, was not associated with learning performance and did not moderate the relationship between caudate volume and learning rate. This multiparametric approach suggests that age-related differences in striatal volume may influence learning proficiency in old age.

Test-retest variability of brain morphometry analysis: an investigation of sequence and coil effects

Background

Precise and reliable brain morphometry analysis is critical for clinical and research purposes. The magnetization-prepared rapid gradient echo (MPRAGE), multi-echo MPRAGE (MEMPRAGE) and magnetization-prepared 2 rapid acquisition gradient echo (MP2RAGE) sequences have all been used to acquire brain structural images, but it is unclear which of these sequences is the most suitable for brain morphometry and whether the number of coil channels (20 or 32) affects scan precision. This study aimed to assess the impact of T1-weighted image acquisition variables (sequence and head coil) on the repeatability of resultant automated volumetric measurements.

Methods

Twenty-four healthy volunteers underwent back-to-back scanning protocols with three sequences and two different coils (i.e., six scanning conditions in total) presented in a randomized order in a single session. MorphoBox prototype and FreeSurfer were used for brain segmentation. Brain structures were divided into cortical and subcortical regions for more precise analysis. The acquired volume and thickness values were used to calculate test-retest variability (TRV) values. TRV values from the six different combinations were compared for total brain structures, total cortical structures, total subcortical structures, and every single structure.

Results

The median TRV value for all brain regions was 1.23% with MorphoBox and 3.14% with FreeSurfer. When using FreeSurfer results to compare the six combinations, for total brain structures volume and total cortical structures volume and thickness, the MEMPRAGE-32 channel combination showed significantly lower TRV values than the others (P<0.01). Similar results were observed with MorphoBox. For total subcortical structures, the MP2RAGE-32 channel combination showed the lowest TRV values with both MorphoBox (lower about 0.01% to 0.17%) and FreeSurfer analyses (lower about 0.02% to 0.37%).

Conclusions

TRV values were generally low, indicating generally high reliability for every region. The MEMPRAGE sequence was the most reliable of the three sequences for total brain structures and cortical structures. However, MP2RAGE was the most reliable for subcortical structures. The 32-channel coil showed better repeatability results than the 20-channel coil.

Eliminating susceptibility induced hyperintensities in T1w MPRAGE brain images at 7 T

INTRODUCTION

At ultrahigh field, local susceptibility induced hyperintensities are pronounced in brain areas close to air-tissue boundaries in the inferior frontal lobe and temporal lobes on T1w MPRAGE images. Resulting from incomplete inversion, these artefacts can introduce biases in brain volumetry and erroneously suggest the existence of local tissular anomaly. We propose a straightforward approach to eliminate these artefacts by applying a shift (Δf_IR) to the center frequency of the adiabatic inversion pulse while widening the bandwidth of the latter by shortening the pulse duration (Δt_IR).

METHODS

An MPRAGE sequence was customized allowing to change the duration (standard: 10,240 μs) and center frequency of the hyperbolic secant inversion RF pulse (IR). All measurements were performed on a 7 T whole body scanner (Siemens, Erlangen, Germany). 13 healthy volunteers (7 female and 6 male, average age (SD) = 38 ± 15 yrs) were recruited for the study, 3 of which were scanned for protocol optimization and the rest for performance evaluation. ΔB0 was mapped through the brain with a gradient echo sequence. The effects of Δf_IR and Δt_IR were studied separately and jointly to determine optimal parameter combinations to achieve the largest spatial extent of complete inversion throughout the brain.

RESULTS

Applying a positive Δf_IR restored inversion efficiency in the inferior frontal and temporal lobes, but also introduced undesired hyperintensities in the anterior temporal lobes. Widening the bandwidth alone could also partially reduce hyperintensities in the frontal area but with a limited efficiency. By simultaneously applying a positive Δf_IR of 300 Hz and shortening Δt_IR by 40%, these artefacts were eliminated across the whole cerebrum.

CONCLUSION

A robust elimination of susceptibility induced hyperintensities near air-tissue boundaries in T1w MPRAGE 7 T brain images is demonstrated. This technique only requires limited MR sequence modifications.

Hippocampal Subfields in Acute and Remitted Depression-an Ultra-High Field Magnetic Resonance Imaging Study

BACKGROUND

Studies investigating hippocampal volume changes after treatment with serotonergic antidepressants in patients with major depressive disorder yielded inconsistent results, and effects on hippocampal subfields are unclear.

METHODS

To detail treatment effects on total hippocampal and subfield volumes, we conducted an open-label study with escitalopram followed by venlafaxine upon nonresponse in 20 unmedicated patients with major depressive disorder. Before and after 12 weeks treatment, we measured total hippocampal formation volumes and subfield volumes with ultra-high field (7 Tesla), T1-weighted, structural magnetic resonance imaging, and FreeSurfer. Twenty-eight remitted patients and 22 healthy subjects were included as controls. We hypothesized to detect increased volumes after treatment in major depressive disorder.

RESULTS

We did not detect treatment-related changes of total hippocampal or subfield volumes in patients with major depressive disorder. Secondary results indicated that the control group of untreated, stable remitted patients, compared with healthy controls, had larger volumes of the right hippocampal-amygdaloid transition area and right fissure at both measurement time points. Depressed patients exhibited larger volumes of the right subiculum compared with healthy controls at MRI-2. Exploratory data analyses indicated lower baseline volumes in the subgroup of remitting (n = 10) vs nonremitting (n = 10) acute patients.

CONCLUSIONS

The results demonstrate that monoaminergic antidepressant treatment in major depressive disorder patients was not associated with volume changes in hippocampal subfields. Studies with larger sample sizes to detect smaller effects as well as other imaging modalities are needed to further assess the impact of antidepressant treatment on hippocampal subfields.

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language.

Prediction of Autopsy Verified Neuropathological Change of Alzheimer's Disease Using Machine Learning and MRI

Background: Alzheimer’s disease (AD) is the most common form of dementia. While neuropathological changes pathognomonic for AD have been defined, early detection of AD prior to cognitive impairment in the clinical setting is still lacking. Pioneer studies applying machine learning to magnetic-resonance imaging (MRI) data to predict mild cognitive impairment (MCI) or AD have yielded high accuracies, however, an algorithm predicting neuropathological change is still lacking. The objective of this study was to compute a prediction model supporting a more distinct diagnostic criterium for AD compared to clinical presentation, allowing identification of hallmark changes even before symptoms occur.

Methods: Autopsy verified neuropathological changes attributed to AD, as described by a combined score for Aβ-peptides, neurofibrillary tangles and neuritic plaques issued by the National Institute on Aging – Alzheimer’s Association (NIAA), the ABC score for AD, were predicted from structural MRI data with RandomForest (RF). MRI scans were performed at least 2 years prior to death. All subjects derive from the prospective Vienna Trans-Danube Aging (VITA) study that targeted all 1750 inhabitants of the age of 75 in the starting year of 2000 in two districts of Vienna and included irregular follow-ups until death, irrespective of clinical symptoms or diagnoses. For 68 subjects MRI as well as neuropathological data were available and 49 subjects (mean age at death: 82.8 ± 2.9, 29 female) with sufficient MRI data quality were enrolled for further statistical analysis using nested cross-validation (CV). The decoding data of the inner loop was used for variable selection and parameter optimization with a fivefold CV design, the new data of the outer loop was used for model validation with optimal settings in a fivefold CV design. The whole procedure was performed ten times and average accuracies with standard deviations were reported.

Results: The most informative ROIs included caudal and rostral anterior cingulate gyrus, entorhinal, fusiform and insular cortex and the subcortical ROIs anterior corpus callosum and the left vessel, a ROI comprising lacunar alterations in inferior putamen and pallidum. The resulting prediction models achieved an average accuracy for a three leveled NIAA AD score of 0.62 within the decoding sets and of 0.61 for validation sets. Higher accuracies of 0.77 for both sets, respectively, were achieved when predicting presence or absence of neuropathological change.

Conclusion: Computer-aided prediction of neuropathological change according to the categorical NIAA score in AD, that currently can only be assessed post-mortem, may facilitate a more distinct and definite categorization of AD dementia. Reliable detection of neuropathological hallmarks of AD would enable risk stratification at an earlier level than prediction of MCI or clinical AD symptoms and advance precision medicine in neuropsychiatry.

Longitudinal Progression Markers of Parkinson's Disease: Current View on Structural Imaging

PURPOSE OF REVIEW

Advances in neuroimaging techniques pave a rich avenue for in vivo progression biomarkers, which can objectively and noninvasively assess the long-term dynamic alterations in the brain of Parkinson's disease (PD) patients. This article reviews recent progress in structural magnetic resonance imaging (MRI) tools to track disease progression in PD, and discusses specific criteria a neuroimaging tool needs to meet to be a progression biomarker of PD and the potential applications of these techniques in PD based on current evidence.

RECENT FINDINGS

Recent longitudinal studies showed that quantitative structural MRI markers derived from T1-weighted, diffusion-weighted, neuromelanin-sensitive, and iron-sensitive imaging have the potential to track disease progression in PD. However, validation of these progression biomarkers is only beginning, and more work is required for multisite validation, the sample size for use in a clinical trial, and drug-responsiveness of most of these biomarkers. At present, the most clinical trial-ready biomarker is free-water diffusion imaging of the substantia nigra and seems well established to be used in disease-modifying studies in PD. A variety of structural imaging biomarkers are promising candidates to be progression biomarkers in PD. Further studies are needed to elucidate the sensitivity, reliability, sample size, and effect of confounding factors of these progression biomarkers.

Within-subject test-retest reliability of the atlas-based cortical volume measurement in the rat brain: A voxel-based morphometry study

BACKGROUND

Various neurological and psychological disorders are related to cortical volume changes in specific brain regions, which can be measured in vivo using structural magnetic resonance imaging (sMRI). There is an increasing interest in MRI studies using rat models, especially in longitudinal studies of brain disorders and pharmacologic interventions. However, morphometric changes observed in sMRI are only meaningful if the measurements are reliable. To date, a systematic evaluation of the test-retest reliability of the morphometric measures in the rat brain is still lacking.

NEW METHOD

We rigorously evaluated the test-retest reliability of morphometric measures derived from the voxel-based morphometry (VBM) analysis. 37 Sprague-Dawley rats were scanned twice at an interval of six hours and the gray matter volume was estimated using the VBM-DARTEL method. The intraclass coefficient, percent volume change and Pearson correlation coefficient were used to evaluate the reliability in 96 subregions of the rat brain.

RESULTS

Most subregions showed excellent test-retest reliabilities within an interval of 6 h while a few regions demonstrated lower reliability, especially in the retrosplenial granular cortex. The results were consistent between different methods of reliability assessment.

COMPARISON WITH EXISTING METHOD

To the best of our knowledge, this is the first study to quantify the test-retest reliability of the VBM measurements of the rat brain.

CONCLUSION

Atlas-based cortical volume of the rat brain can be reliably estimated using the VBM-DARTEL method in most subregions. However, findings in subregions with lower reliability must be interpreted with caution.

Cortical Thickness Estimations of FreeSurfer and the CAT12 Toolbox in Patients with Alzheimer's Disease and Healthy Controls

BACKGROUND AND PURPOSE

Automated cortical thickness (CT) measurements are often used to assess gray matter changes in the healthy and diseased human brain. The FreeSurfer software is frequently applied for this type of analysis. The computational anatomy toolbox (CAT12) for SPM, which offers a fast and easy‐to‐use alternative approach, was recently made available.

METHODS

In this study, we compared region of interest (ROI)‐wise CT estimations of the surface‐based FreeSurfer 6 (FS6) software and the volume‐based CAT12 toolbox for SPM using 44 elderly healthy female control subjects (HC). In addition, these 44 HCs from the cross‐sectional analysis and 34 age‐ and sex‐matched patients with Alzheimer's disease (AD) were used to assess the potential of detecting group differences for each method. Finally, a test‐retest analysis was conducted using 19 HC subjects. All data were taken from the OASIS database and MRI scans were recorded at 1.5 Tesla.

RESULTS

A strong correlation was observed between both methods in terms of ROI mean CT estimates (R² = .83). However, CAT12 delivered significantly higher CT estimations in 32 of the 34 ROIs, indicating a systematic difference between both approaches. Furthermore, both methods were able to reliably detect atrophic brain areas in AD subjects, with the highest decreases in temporal areas. Finally, FS6 as well as CAT12 showed excellent test‐retest variability scores.

CONCLUSION

Although CT estimations were systematically higher for CAT12, this study provides evidence that this new toolbox delivers accurate and robust CT estimates and can be considered a fast and reliable alternative to FreeSurfer.

Future Brain and Spinal Cord Volumetric Imaging in the Clinic for Monitoring Treatment Response in MS

PURPOSE OF REVIEW

Volumetric analysis of brain imaging has emerged as a standard approach used in clinical research, e.g., in the field of multiple sclerosis (MS), but its application in individual disease course monitoring is still hampered by biological and technical limitations. This review summarizes novel developments in volumetric imaging on the road towards clinical application to eventually monitor treatment response in patients with MS.

RECENT FINDINGS

In addition to the assessment of whole-brain volume changes, recent work was focused on the volumetry of specific compartments and substructures of the central nervous system (CNS) in MS. This included volumetric imaging of the deep brain structures and of the spinal cord white and gray matter. Volume changes of the latter indeed independently correlate with clinical outcome measures especially in progressive MS. Ultrahigh field MRI and quantitative MRI added to this trend by providing a better visualization of small compartments on highly resolving MR images as well as microstructural information. New developments in volumetric imaging have the potential to improve sensitivity as well as specificity in detecting and hence monitoring disease-related CNS volume changes in MS.

Primatologist: A modular segmentation pipeline for macaque brain morphometry

Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Primatologist, for multi-region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation-maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre-processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2-weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non-linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated.

7T-guided super-resolution of 3T MRI

PURPOSE

High-resolution MR images can depict rich details of brain anatomical structures and show subtle changes in longitudinal data. 7T MRI scanners can acquire MR images with higher resolution and better tissue contrast than the routine 3T MRI scanners. However, 7T MRI scanners are currently more expensive and less available in clinical and research centers. To this end, we propose a method to generate super-resolution 3T MRI that resembles 7T MRI, which is called as 7T-like MR image in this paper.

METHODS

First, we propose a mapping from 3T MRI to 7T MRI space, using regression random forest. The mapped 3T MR images serve as intermediate results with similar appearance as 7T MR images. Second, we predict the final higher resolution 7T-like MR images based on sparse representation, using paired local dictionaries for both the mapped 3T MR images and 7T MR images.

RESULTS

Based on 15 subjects with both 3T and 7T MR images, the predicted 7T-like MR images by our method can best match the ground-truth 7T MR images, compared to other methods. Meanwhile, the experiment on brain tissue segmentation shows that our 7T-like MR images lead to the highest accuracy in the segmentation of WM, GM, and CSF brain tissues, compared to segmentations of 3T MR images as well as the reconstructed 7T-like MR images by other methods.

CONCLUSIONS

We propose a novel method for prediction of high-resolution 7T-like MR images from low-resolution 3T MR images. Our predicted 7T-like MR images demonstrate better spatial resolution compared to 3T MR images, as well as prediction results by other comparison methods. Such high-quality 7T-like MR images could better facilitate disease diagnosis and intervention.

Serotonin and neuroplasticity - Links between molecular, functional and structural pathophysiology in depression

Serotonin modulates neuroplasticity, especially during early life, and dysfunctions in both systems likewise contribute to pathophysiology of depression. Recent findings demonstrate that serotonin reuptake inhibitors trigger reactivation of juvenile-like neuroplasticity. How these findings translate to clinical antidepressant treatment in major depressive disorder remains unclear. With this review, we link preclinical with clinical work on serotonin and neuroplasticity to bring two pathophysiologic models in clinical depression closer together. Dysfunctional developmental plasticity impacts on later-life cognitive and emotional functions, changes of synaptic serotonin levels and receptor levels are coupled with altered synaptic plasticity and neurogenesis. Structural magnetic resonance imaging in patients reveals disease-state-specific reductions of gray matter, a marker of neuroplasticity, and reversibility upon selective serotonin reuptake inhibitor treatment. Translational evidence from magnetic resonance imaging in animals support that reduced densities and sizes of neurons and reduced hippocampal volumes in depressive patients could be attributable to changes of serotonergic neuroplasticity. Since ketamine, physical exercise or learning enhance neuroplasticity, combinatory paradigms with selective serotonin reuptake inhibitors could enhance clinical treatment of depression.

Quantification of local geometric distortion in structural magnetic resonance images: Application to ultra-high fields

Ultra-high field magnetic resonance imaging (MRI) provides superior visualization of brain structures compared to lower fields, but images may be prone to severe geometric inhomogeneity. We propose to quantify local geometric distortion at ultra-high fields in in vivo datasets of human subjects scanned at both ultra-high field and lower fields. By using the displacement field derived from nonlinear image registration between images of the same subject, focal areas of spatial uncertainty are quantified. Through group and subject-specific analysis, we were able to identify regions systematically affected by geometric distortion at air-tissue interfaces prone to magnetic susceptibility, where the gradient coil non-linearity occurs in the occipital and suboccipital regions, as well as with distance from image isocenter. The derived displacement maps, quantified in millimeters, can be used to prospectively evaluate subject-specific local spatial uncertainty that should be taken into account in neuroimaging studies, and also for clinical applications like stereotactic neurosurgery where accuracy is critical. Validation with manual fiducial displacement demonstrated excellent correlation and agreement. Our results point to the need for site-specific calibration of geometric inhomogeneity. Our methodology provides a framework to permit prospective evaluation of the effect of MRI sequences, distortion correction techniques, and scanner hardware/software upgrades on geometric distortion.

MRI postprocessing in presurgical evaluation

PURPOSE OF REVIEW

Advanced MRI postprocessing techniques are increasingly used to complement visual analysis and elucidate structural epileptogenic lesions. This review summarizes recent developments in MRI postprocessing in the context of epilepsy presurgical evaluation, with the focus on patients with unremarkable MRI by visual analysis (i.e. 'nonlesional' MRI).

RECENT FINDINGS

Various methods of MRI postprocessing have been reported to show additional clinical values in the following areas: lesion detection on an individual level; lesion confirmation for reducing the risk of over reading the MRI; detection of sulcal/gyral morphologic changes that are particularly difficult for visual analysis; and delineation of cortical abnormalities extending beyond the visible lesion. Future directions to improve the performance of MRI postprocessing include using higher magnetic field strength for better signal-to-noise ratio and contrast-to-noise ratio adopting a multicontrast frame work and integration with other noninvasive modalities.

SUMMARY

MRI postprocessing can provide essential value to increase the yield of structural MRI and should be included as part of the presurgical evaluation of nonlesional epilepsies. MRI postprocessing allows for more accurate identification/delineation of cortical abnormalities, which should then be more confidently targeted and mapped.

Imaging the neuroplastic effects of ketamine with VBM and the necessity of placebo control

In the last years a plethora of studies have investigated morphological changes induced by behavioural or pharmacological interventions using structural T1-weighted MRI and voxel-based morphometry (VBM). Ketamine is thought to exert its antidepressant action by restoring neuroplasticity. In order to test for acute impact of a single ketamine infusion on grey matter volume we performed a placebo-controlled, double-blind investigation in healthy volunteers using VBM. 28 healthy individuals underwent two MRI sessions within a timeframe of 2 weeks, each consisting of two structural T1-weighted MRIs within a single session, one before and one 45min after infusion of S-ketamine (bolus of 0.11mg/kg, followed by an maintenance infusion of 0.12mg/kg) or placebo (0.9% NaCl infusion) using a crossover design. In the repeated-measures ANOVA with time (post-infusion/pre-infusion) and medication (placebo/ketamine) as factors, no significant effect of interaction and no effect of medication was found (FWE-corrected). Importantly, further post-hoc t-tests revealed a strong "decrease" of grey matter both in the placebo and the ketamine condition over time. This effect was evident mainly in frontal and temporal regions bilaterally with t-values ranging from 4.95 to 5.31 (FWE-corrected at p<0.05 voxel level). The vulnerabilities of VBM have been repeatedly demonstrated, with reports of influence of blood flow, tissue water and direct effects of pharmacological compounds on the MRI signal. Here again, we highlight that the relationship between intervention and VBM results is apparently subject to a number of physiological influences, which are partly unknown. Future studies focusing on the effects of ketamine on grey matter should try to integrate known influential factors such as blood flow into analysis. Furthermore, the results of this study highlight the importance of a carefully performed placebo condition in pharmacological fMRI studies.

The traveling heads: multicenter brain imaging at 7 Tesla

OBJECTIVE

This study evaluates the inter-site and intra-site reproducibility of 7 Tesla brain imaging and compares it to literature values for other field strengths.

MATERIALS AND METHODS

The same two subjects were imaged at eight different 7 T sites. MP2RAGE, TSE, TOF, SWI, EPI as well as B1 and B0 field maps were analyzed quantitatively to assess inter-site reproducibility. Intra-site reproducibility was measured with rescans at three sites.

RESULTS

Quantitative measures of MP2RAGE scans showed high agreement. Inter-site and intra-site reproducibility errors were comparable to 1.5 and 3 T. Other sequences also showed high reproducibility between the sites, but differences were also revealed. The different RF coils used were the main source for systematic differences between the sites.

CONCLUSION

Our results show for the first time that multi-center brain imaging studies of the supratentorial brain can be performed at 7 T with high reproducibility and similar reliability as at 3T. This study develops the basis for future large-scale 7 T multi-site studies.

Ultrahigh field MRI in clinical neuroimmunology: a potential contribution to improved diagnostics and personalised disease management

Conventional magnetic resonance imaging (MRI) at 1.5 Tesla (T) is limited by modest spatial resolution and signal-to-noise ratio (SNR), impeding the identification and classification of inflammatory central nervous system changes in current clinical practice. Gaining from enhanced susceptibility effects and improved SNR, ultrahigh field MRI at 7 T depicts inflammatory brain lesions in great detail. This review summarises recent reports on 7 T MRI in neuroinflammatory diseases and addresses the question as to whether ultrahigh field MRI may eventually improve clinical decision-making and personalised disease management.