System for integrated neuroimaging analysis and processing of structure

Initial evidence of abnormal brain plasticity in anorexia nervosa: an ultra-high field study

Anorexia Nervosa has been associated with white matter abnormalities implicating subcortical abnormal myelination. Extending these findings to intracortical myelin has been challenging but ultra-high field neuroimaging offers new methodological opportunities. To test the integrity of intracortical myelin in AN we used 7 T neuroimaging to acquire T1-weighted images optimized for intracortical myelin from seven females with AN (age range: 18-33) and 11 healthy females (age range: 23-32). Intracortical T₁ values (inverse index of myelin concentration) were extracted from 148 cortical regions at ten depth-levels across the cortical ribbon. Across all cortical regions, these levels were averaged to generate estimates of total intracortical myelin concentration and were clustered using principal component analyses into two clusters; the outer cluster comprised T₁ values across depth-levels ranging from the CSF boundary to the middle of the cortical regions and the inner cluster comprised T₁ values across depth-levels ranging from the middle of the cortical regions to the gray/white matter boundary. Individuals with AN exhibited higher T₁ values (i.e., decreased intracortical myelin concentration) in all three metrics. It remains to be established if these abnormalities result from undernutrition or specific lipid nutritional imbalances, or are trait markers; and whether they may contribute to neurobiological deficits seen in AN.

Multi-layer analysis of quantitative 7 T magnetic resonance imaging in the cortex of multiple sclerosis patients reveals pathology associated with disability

BACKGROUND

Cortical demyelination is a relevant aspect of tissue damage in multiple sclerosis (MS). Microstructural changes may affect each layer in the cortex differently.

OBJECTIVES

To evaluate the sensitivity of quantitative magnetic resonance imaging (qMRI) measurements on cortical layers as clinically accessible biomarkers of grey matter (GM) pathology.

METHODS

Forty-five participants with MS underwent 7 T magnetic resonance imaging (MRI) of the brain. Magnetization prepared two rapid acquisition gradient echoes (MP2RAGE) was processed for T1-weighted images and a T1 map. Multi-echo gradient echo images were processed for quantitative susceptibility and R2* maps. Cortical GM volumes were segmented into four cortical layers, and relaxometry metrics were calculated within and between these layers.

RESULTS

Significant correlations were found for disability scales and multi-layer metrics, for example, Expanded Disability Status Scale (EDSS) and peak height (PH) in the subpial (T1: ρ = -0.372, p < 0.050) and inner (R2*: ρ = -0.359, p < 0.050) cortical layers. Multivariate regression showed interdependency between atrophy and cortical metrics in some instances, but an independent relationship between cortical metrics and disability in others.

CONCLUSION

Cortical layer 7 T qMRI analyses reveal layer-specific relationships with disability in MS and allow emergence of clinically relevant associations that are hidden when analysing the full cortex.

Resolving multisensory and attentional influences across cortical depth in sensory cortices

In our environment, our senses are bombarded with a myriad of signals, only a subset of which is relevant for our goals. Using sub-millimeter-resolution fMRI at 7T, we resolved BOLD-response and activation patterns across cortical depth in early sensory cortices to auditory, visual and audiovisual stimuli under auditory or visual attention. In visual cortices, auditory stimulation induced widespread inhibition irrespective of attention, whereas auditory relative to visual attention suppressed mainly central visual field representations. In auditory cortices, visual stimulation suppressed activations, but amplified responses to concurrent auditory stimuli, in a patchy topography. Critically, multisensory interactions in auditory cortices were stronger in deeper laminae, while attentional influences were greatest at the surface. These distinct depth-dependent profiles suggest that multisensory and attentional mechanisms regulate sensory processing via partly distinct circuitries. Our findings are crucial for understanding how the brain regulates information flow across senses to interact with our complex multisensory world.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Comparing fully automated state-of-the-art cerebellum parcellation from magnetic resonance images

The human cerebellum plays an essential role in motor control, is involved in cognitive function (i.e., attention, working memory, and language), and helps to regulate emotional responses. Quantitative in-vivo assessment of the cerebellum is important in the study of several neurological diseases including cerebellar ataxia, autism, and schizophrenia. Different structural subdivisions of the cerebellum have been shown to correlate with differing pathologies. To further understand these pathologies, it is helpful to automatically parcellate the cerebellum at the highest fidelity possible. In this paper, we coordinated with colleagues around the world to evaluate automated cerebellum parcellation algorithms on two clinical cohorts showing that the cerebellum can be parcellated to a high accuracy by newer methods. We characterize these various methods at four hierarchical levels: coarse (i.e., whole cerebellum and gross structures), lobe, subdivisions of the vermis, and the lobules. Due to the number of labels, the hierarchy of labels, the number of algorithms, and the two cohorts, we have restricted our analyses to the Dice measure of overlap. Under these conditions, machine learning based methods provide a collection of strategies that are efficient and deliver parcellations of a high standard across both cohorts, surpassing previous work in the area. In conjunction with the rank-sum computation, we identified an overall winning method.

Depth-dependent intracortical myelin organization in the living human brain determined by in vivo ultra-high field magnetic resonance imaging

BACKGROUND

Intracortical myelin is a key determinant of neuronal synchrony and plasticity that underpin optimal brain function. Magnetic resonance imaging (MRI) facilitates the examination of intracortical myelin but presents with methodological challenges. Here we describe a whole-brain approach for the in vivo investigation of intracortical myelin in the human brain using ultra-high field MRI.

METHODS

Twenty-five healthy adults were imaged in a 7 Tesla MRI scanner using diffusion-weighted imaging and a T₁-weighted sequence optimized for intracortical myelin contrast. Using an automated pipeline, T₁ values were extracted at 20 depth-levels from each of 148 cortical regions. In each cortical region, T₁ values were used to infer myelin concentration and to construct a non-linearity index as a measure the spatial distribution of myelin across the cortical ribbon. The relationship of myelin concentration and the non-linearity index with other neuroanatomical properties were investigated. Five patients with multiple sclerosis were also assessed using the same protocol as positive controls.

RESULTS

Intracortical T₁ values decreased between the outer brain surface and the gray-white matter boundary following a slope that showed a slight leveling between 50% and 75% of cortical depth. Higher-order regions in the prefrontal, cingulate and insular cortices, displayed higher non-linearity indices than sensorimotor regions. Across all regions, there was a positive association between T₁ values and non-linearity indices (P < 10^-5). Both T₁ values (P < 10^-5) and non-linearity indices (P < 10^-15) were associated with cortical thickness. Higher myelin concentration but only in the deepest cortical levels was associated with increased subcortical fractional anisotropy (P = 0.05).

CONCLUSIONS

We demonstrate the usefulness of an automatic, whole-brain method to perform depth-dependent examination of intracortical myelin organization. The extracted metrics, T₁ values and the non-linearity index, have characteristic patterns across cortical regions, and are associated with thickness and underlying white matter microstructure.

Vascular Abnormalities within Normal Appearing Tissue in Chronic Traumatic Brain Injury

Magnetic resonance imaging (MRI) is a powerful tool for visualizing traumatic brain injury(TBI)-related lesions. Trauma-induced encephalomalacia is frequently identified by its hyperintense appearance on fluid-attenuated inversion recovery (FLAIR) sequences. In addition to parenchymal lesions, TBI commonly results in cerebral microvascular injury, but its anatomical relationship to parenchymal encephalomalacia is not well characterized. The current study utilized a multi-modal MRI protocol to assess microstructural tissue integrity (by mean diffusivity [MD] and fractional aniosotropy [FA]) and altered vascular function (by cerebral blood flow [CBF] and cerebral vascular reactivity [CVR]) within regions of visible encephalomalacia and normal appearing tissue in 27 chronic TBI (minimum 6 months post-injury) subjects. Fifteen subjects had visible encephalomalacias whereas 12 did not have evident lesions on MRI. Imaging from 14 age-matched healthy volunteers were used as controls. CBF was assessed by arterial spin labeling (ASL) and CVR by measuring the change in blood-oxygen-level-dependent (BOLD) MRI during a hypercapnia challenge. There was a significant reduction in FA, CBF, and CVR with a complementary increase in MD within regions of FLAIR-visible encephalomalacia (p < 0.05 for all comparisons). In normal-appearing brain regions, only CVR was significantly reduced relative to controls (p < 0.05). These findings indicate that vascular dysfunction represents a TBI endophenotype that is distinct from structural injury detected using conventional MRI, may be present even in the absence of visible structural injury, and persists long after trauma. CVR may serve as a useful diagnostic and pharmacodynamic imaging biomarker of traumatic microvascular injury.

Disrupted Gamma Synchrony after Mild Traumatic Brain Injury and Its Correlation with White Matter Abnormality

Mild traumatic brain injury (mTBI) has been firmly associated with disrupted white matter integrity due to induced white matter damage and degeneration. However, comparatively less is known about the changes of the intrinsic functional connectivity mediated via neural synchronization in the brain after mTBI. Moreover, despite the presumed link between structural and functional connectivity, no existing studies in mTBI have demonstrated clear association between the structural abnormality of white matter axons and the disruption of neural synchronization. To investigate these questions, we recorded resting state EEG and diffusion tensor imaging (DTI) from a cohort of military service members. A newly developed synchronization measure, the weighted phase lag index was applied on the EEG data for estimating neural synchronization. Fractional anisotropy was computed from the DTI data for estimating white matter integrity. Fifteen service members with a history of mTBI within the past 3 years were compared to 22 demographically similar controls who reported no history of head injury. We observed that synchronization at low-gamma frequency band (25–40 Hz) across scalp regions was significantly decreased in mTBI cases compared with controls. The synchronization in theta (4–7 Hz), alpha (8–13 Hz), and beta (15–23 Hz) frequency bands were not significantly different between the two groups. In addition, we found that across mTBI cases, the disrupted synchronization at low-gamma frequency was significantly correlated with the white matter integrity of the inferior cerebellar peduncle, which was also significantly reduced in the mTBI group. These findings demonstrate an initial correlation between the impairment of white matter integrity and alterations in EEG synchronization in the brain after mTBI. The results also suggest that disruption of intrinsic neural synchronization at low-gamma frequency may be a characteristic functional pathology following mTBI and may prove useful for developing better methods of diagnosis and treatment.

Virtual Human Models for Electromagnetic Studies and Their Applications

Numerical simulation of electromagnetic, thermal, and mechanical responses of the human body to different stimuli in magnetic resonance imaging safety, antenna research, electromagnetic tomography, and electromagnetic stimulation is currently limited by the availability of anatomically adequate and numerically efficient cross-platform computational models or "virtual humans." The objective of this study is to provide a comprehensive review of modern human models and body region models available in the field and their important features.

Consistent cortical reconstruction and multi-atlas brain segmentation

Whole brain segmentation and cortical surface reconstruction are two essential techniques for investigating the human brain. Spatial inconsistences, which can hinder further integrated analyses of brain structure, can result due to these two tasks typically being conducted independently of each other. FreeSurfer obtains self-consistent whole brain segmentations and cortical surfaces. It starts with subcortical segmentation, then carries out cortical surface reconstruction, and ends with cortical segmentation and labeling. However, this "segmentation to surface to parcellation" strategy has shown limitations in various cohorts such as older populations with large ventricles. In this work, we propose a novel "multi-atlas segmentation to surface" method called Multi-atlas CRUISE (MaCRUISE), which achieves self-consistent whole brain segmentations and cortical surfaces by combining multi-atlas segmentation with the cortical reconstruction method CRUISE. A modification called MaCRUISE(+) is designed to perform well when white matter lesions are present. Comparing to the benchmarks CRUISE and FreeSurfer, the surface accuracy of MaCRUISE and MaCRUISE(+) is validated using two independent datasets with expertly placed cortical landmarks. A third independent dataset with expertly delineated volumetric labels is employed to compare segmentation performance. Finally, 200MR volumetric images from an older adult sample are used to assess the robustness of MaCRUISE and FreeSurfer. The advantages of MaCRUISE are: (1) MaCRUISE constructs self-consistent voxelwise segmentations and cortical surfaces, while MaCRUISE(+) is robust to white matter pathology. (2) MaCRUISE achieves more accurate whole brain segmentations than independently conducting the multi-atlas segmentation. (3) MaCRUISE is comparable in accuracy to FreeSurfer (when FreeSurfer does not exhibit global failures) while achieving greater robustness across an older adult population. MaCRUISE has been made freely available in open source.

White matter structural connectivity changes correlate with epilepsy duration in temporal lobe epilepsy

PURPOSE

Temporal lobe epilepsy (TLE) is thought to be a network disease and structural changes using diffusion tensor imaging (DTI) have been shown. However, lateralized differences in the structural integrity of TLE, as well as changes in structural integrity with longer disease duration, have not been well defined.

METHODS

We examined the fractional anisotropy (FA) and mean diffusivity (MD) in the hippocampus, as well as its primary (cingulum and fornix) and remote (uncinate and external capsule) connections in both right and left TLE. Changes in diffusion measures over the disease course were examined by correlating FA and MD in the various structures with epilepsy duration. The potential for each measure of anisotropy and diffusivity as a marker of TLE laterality was investigated using random forest (RF) analysis.

RESULTS

MD was increased in the bilateral hippocampus, cingulum, fornix and the right external capsule in both left and right TLE compared to controls. In addition, left TLE exhibited an increased MD in the ipsilateral uncinate fasciculus and bilateral external capsules. A decrease in FA was seen in the left cingulum in left TLE. RF analysis demonstrated that MD of the right hippocampus and FA of the left external capsule were important predictors of TLE laterality. An association of increased MD with epilepsy duration was seen in the left hippocampus in left TLE.

CONCLUSION

Evidence of disrupted white matter architecture in the hippocampus and its primary and remote connections were demonstrated in TLE. While changes in the hippocampus and cingulum were more prominent in right TLE, remote changes were more prominent in left TLE. MD of the right hippocampus and FA of the left external capsule were found to be the strongest structural predictors of TLE laterality. Changes associated with duration of epilepsy indicated that changes in structural integrity may be progressive over the disease course. This study illustrates the potential of structural diffusion tensor imaging in elucidating pathophysiology, enhancing diagnosis and assisting prognostication.

Predictors of Neurocognitive Syndromes in Combat Veterans

Traumatic brain injury, depression and posttraumatic stress disorder (PTSD) are neurocognitive syndromes often associated with impairment of physical and mental health, as well as functional status. These syndromes are also frequent in military service members (SMs) after combat, although their presentation is often delayed until months after their return. The objective of this prospective cohort study was the identification of independent predictors of neurocognitive syndromes upon return from deployment could facilitate early intervention to prevent disability. We completed a comprehensive baseline assessment, followed by serial evaluations at three, six, and 12 months, to assess for new-onset PTSD, depression, or postconcussive syndrome (PCS) in order to identify baseline factors most strongly associated with subsequent neurocognitive syndromes. On serial follow-up, seven participants developed at least one neurocognitive syndrome: five with PTSD, one with depression and PTSD, and one with PCS. On univariate analysis, 60 items were associated with syndrome development at p < 0.15. Decision trees and ensemble tree multivariate models yielded four common independent predictors of PTSD: right superior longitudinal fasciculus tract volume on MRI; resting state connectivity between the right amygdala and left superior temporal gyrus (BA41/42) on functional MRI; and single nucleotide polymorphisms in the genes coding for myelin basic protein as well as brain-derived neurotrophic factor. Our findings require follow-up studies with greater sample size and suggest that neuroimaging and molecular biomarkers may help distinguish those at high risk for post-deployment neurocognitive syndromes.

Structural-functional coupling changes in temporal lobe epilepsy

Alterations in both structural connectivity (SC) and functional connectivity (FC) have been reported in temporal lobe epilepsy (TLE). However, the relationship between FC and SC remains less understood. This study used functional connectivity MRI and diffusion tensor imaging to examine coupling of FC and SC within the limbic network of TLE, as well as its relation to epilepsy duration, regional changes, and disease laterality in 14 patients with left TLE, 10 with right TLE, and 11 healthy controls. Structural and functional networks were separately constructed and the correlation estimated between structural and functional connectivity. This measure of SC-FC coupling was compared between left/right TLE and controls, and correlated with epilepsy duration. Elastic net regression was used to investigate regional structural and functional changes associated with SC-FC coupling. SC-FC coupling was decreased in left TLE compared to controls, and accompanied by reductions in FC for left and right TLE and in SC for left TLE. When examined in relation to disease duration, an increase in SC-FC coupling with longer epilepsy duration was observed, associated predominantly with structural loss of the fusiform and frontal inferior orbital gyrus in left TLE and functional hub redistribution in right TLE. These results suggest that decoupling between structural and functional networks in TLE is modulated by several factors, including epilepsy duration and regional changes in the fusiform, frontal inferior orbital gyrus, posterior cingulate, and hippocampus. SC-FC coupling may provide a more sensitive biomarker of disease burden in TLE than biomarkers based on single imaging modalities.

Functional network mirrored in the prefrontal cortex, caudate nucleus, and thalamus: high-resolution functional imaging and structural connectivity

Despite myriads of studies on a parallel organization of cortico-striatal-thalamo-cortical loops, direct evidence of this has been lacking for the healthy human brain. Here, we scrutinize the functional specificity of the cortico-subcortical loops depending on varying levels of cognitive hierarchy as well as their structural connectivity with high-resolution fMRI and diffusion-weighted MRI (dMRI) at 7 tesla. Three levels of cognitive hierarchy were implemented in two domains: second language and nonlanguage. In fMRI, for the higher level, activations were found in the ventroanterior portion of the prefrontal cortex (PFC), the head of the caudate nucleus (CN), and the ventral anterior nucleus (VA) in the thalamus. Conversely, for the lower level, activations were located in the posterior region of the PFC, the body of the CN, and the medial dorsal nucleus (MD) in the thalamus. This gradient pattern of activations was furthermore shown to be tenable by the parallel connectivity in dMRI tractography connecting the anterior regions of the PFC with the head of the CN and the VA in the thalamus, whereas the posterior activations of the PFC were linked to the body of the CN and the MD in the thalamus. This is the first human in vivo study combining fMRI and dMRI showing that the functional specificity is mirrored within the cortico-subcortical loop substantiated by parallel networks.

Connecting combat-related mild traumatic brain injury with posttraumatic stress disorder symptoms through brain imaging

Mild traumatic brain injury (mTBI) and posttraumatic stress disorder (PTSD) may share common symptom and neuropsychological profiles in military service members (SMs) following deployment; while a connection between the two conditions is plausible, the relationship between them has been difficult to discern. The intent of this report is to enhance our understanding of the relationship between findings on structural and functional brain imaging and symptoms of PTSD. Within a cohort of SMs who did not meet criteria for PTSD but were willing to complete a comprehensive assessment within 2 months of their return from combat deployment, we conducted a nested case-control analysis comparing those with combat-related mTBI to age/gender-matched controls with diffusion tensor imaging, resting state functional magnetic resonance imaging and a range of psychological measures. We report degraded white matter integrity in those with a history of combat mTBI, and a positive correlation between the white matter microstructure and default mode network (DMN) connectivity. Higher clinician-administered and self-reported subthreshold PTSD symptoms were reported in those with combat mTBI. Our findings offer a potential mechanism through which mTBI may alter brain function, and in turn, contribute to PTSD symptoms.