Gated cardiac CT in infants: What can we expect from deep learning image reconstruction algorithm?

BACKGROUND

ECG-gated cardiac CT is now widely used in infants with congenital heart disease (CHD). Deep Learning Image Reconstruction (DLIR) could improve image quality while minimizing the radiation dose.

OBJECTIVES

To define the potential dose reduction using DLIR with an anthropomorphic phantom.

METHOD

An anthropomorphic pediatric phantom was scanned with an ECG-gated cardiac CT at four dose levels. Images were reconstructed with an iterative and a deep-learning reconstruction algorithm (ASIR-V and DLIR). Detectability of high-contrast vessels were computed using a mathematical observer. Discrimination between two vessels was assessed by measuring the CT spatial resolution. The potential dose reduction while keeping a similar level of image quality was assessed.

RESULTS

DLIR-H enhances detectability by 2.4% and discrimination performances by 20.9% in comparison with ASIR-V 50. To maintain a similar level of detection, the dose could be reduced by 64% using high-strength DLIR in comparison with ASIR-V50.

CONCLUSION

DLIR offers the potential for a substantial dose reduction while preserving image quality compared to ASIR-V.

Associations between antipsychotics-induced weight gain and brain networks of impulsivity

Given the unpredictable rapid onset and ubiquitous consequences of weight gain induced by antipsychotics, there is a pressing need to get insights into the underlying processes at the brain system level that will allow stratification of "at risk" patients. The pathophysiological hypothesis at hand is focused on brain networks governing impulsivity that are modulated by neuro-inflammatory processes. To this aim, we investigated brain anatomy and functional connectivity in patients with early psychosis (median age: 23 years, IQR = 21-27) using anthropometric data and magnetic resonance imaging acquired one month to one year after initiation of AP medication. Our analyses included 19 patients with high and rapid weight gain (i.e., ≥5% from baseline weight after one month) and 23 patients with low weight gain (i.e., <5% from baseline weight after one month). We replicated our analyses in young (26 years, IQR = 22-33, N = 102) and middle-aged (56 years, IQR = 51-62, N = 875) healthy individuals from the general population. In early psychosis patients, higher weight gain was associated with poor impulse control score (β = 1.35; P = 0.03). Here, the observed brain differences comprised nodes of impulsivity networks - reduced frontal lobe grey matter volume (Pcorrected = 0.007) and higher striatal volume (Pcorrected = 0.048) paralleled by disruption of fronto-striatal functional connectivity (R = -0.32; P = 0.04). Weight gain was associated with the inflammatory biomarker plasminogen activator inhibitor-1 (β = 4.9, P = 0.002). There was no significant association between increased BMI or weight gain and brain anatomy characteristics in both cohorts of young and middle-aged healthy individuals. Our findings support the notion of weight gain in treated psychotic patients associated with poor impulse control, impulsivity-related brain networks and chronic inflammation.

Matrix metalloproteinase 9 (MMP-9) activity, hippocampal extracellular free water, and cognitive deficits are associated with each other in early phase psychosis

Increasing evidence points toward the role of the extracellular matrix, specifically matrix metalloproteinase 9 (MMP-9), in the pathophysiology of psychosis. MMP-9 is a critical regulator of the crosstalk between peripheral and central inflammation, extracellular matrix remodeling, hippocampal development, synaptic pruning, and neuroplasticity. Here, we aim to characterize the relationship between plasma MMP-9 activity, hippocampal microstructure, and cognition in healthy individuals and individuals with early phase psychosis. We collected clinical, blood, and structural and diffusion-weighted magnetic resonance imaging data from 39 individuals with early phase psychosis and 44 age and sex-matched healthy individuals. We measured MMP-9 plasma activity, hippocampal extracellular free water (FW) levels, and hippocampal volumes. We used regression analyses to compare MMP-9 activity, hippocampal FW, and volumes between groups. We then examined associations between MMP-9 activity, FW levels, hippocampal volumes, and cognitive performance assessed with the MATRICS battery. All analyses were controlled for age, sex, body mass index, cigarette smoking, and years of education. Individuals with early phase psychosis demonstrated higher MMP-9 activity (p < 0.0002), higher left (p < 0.05) and right (p < 0.05) hippocampal FW levels, and lower left (p < 0.05) and right (p < 0.05) hippocampal volume than healthy individuals. MMP-9 activity correlated positively with hippocampal FW levels (all participants and individuals with early phase psychosis) and negatively with hippocampal volumes (all participants and healthy individuals). Higher MMP-9 activity and higher hippocampal FW levels were associated with slower processing speed and worse working memory performance in all participants. Our findings show an association between MMP-9 activity and hippocampal microstructural alterations in psychosis and an association between MMP-9 activity and cognitive performance. Further, more extensive longitudinal studies should examine the therapeutic potential of MMP-9 modulators in psychosis.

Sex-specific interactions between stress axis and redox balance are associated with internalizing symptoms and brain white matter microstructure in adolescents

Adolescence is marked by the maturation of systems involved in emotional regulation and by an increased risk for internalizing disorders (anxiety/depression), especially in females. Hypothalamic-pituitary-adrenal (HPA)-axis function and redox homeostasis (balance between reactive oxygen species and antioxidants) have both been associated with internalizing disorders and may represent critical factors for the development of brain networks of emotional regulation. However, sex-specific interactions between these factors and internalizing symptoms and their link with brain maturation remain unexplored. We investigated in a cohort of adolescents aged 13-15 from the general population (n = 69) whether sex-differences in internalizing symptoms were associated with the glutathione (GSH)-redox cycle homeostasis and HPA-axis function and if these parameters were associated with brain white matter microstructure development. Female adolescents displayed higher levels of internalizing symptoms, GSH-peroxidase (GPx) activity and cortisol/11-deoxycortisol ratio than males. There was a strong correlation between GPx and GSH-reductase (Gred) activities in females only. The cortisol/11-deoxycortisol ratio, related to the HPA-axis activity, was associated with internalizing symptoms in both sexes, whereas GPx activity was associated with internalizing symptoms in females specifically. The cortisol/11-deoxycortisol ratio mediated sex-differences in internalizing symptoms and the association between anxiety and GPx activity in females specifically. In females, GPx activity was positively associated with generalized fractional anisotropy in widespread white matter brain regions. We found that higher levels of internalizing symptoms in female adolescents than in males relate to sex-differences in HPA-axis function. In females, our results suggest an important interplay between HPA-axis function and GSH-homeostasis, a parameter strongly associated with brain white matter microstructure.

Morphometric and microstructural characteristics of hippocampal subfields in mesial temporal lobe epilepsy and their correlates with mnemonic discrimination

Introduction

Pattern separation (PS) is a fundamental aspect of memory creation that defines the ability to transform similar memory representations into distinct ones, so they do not overlap when storing and retrieving them. Experimental evidence in animal models and the study of other human pathologies have demonstrated the role of the hippocampus in PS, in particular of the dentate gyrus (DG) and CA3. Patients with mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HE) commonly report mnemonic deficits that have been associated with failures in PS. However, the link between these impairments and the integrity of the hippocampal subfields in these patients has not yet been determined. The aim of this work is to explore the association between the ability to perform mnemonic functions and the integrity of hippocampal CA1, CA3, and DG in patients with unilateral MTLE-HE.

Method

To reach this goal we evaluated the memory of patients with an improved object mnemonic similarity test. We then analyzed the hippocampal complex structural and microstructural integrity using diffusion weighted imaging.

Results

Our results indicate that patients with unilateral MTLE-HE present alterations in both volume and microstructural properties at the level of the hippocampal subfields DG, CA1, CA3, and the subiculum, that sometimes depend on the lateralization of their epileptic focus. However, none of the specific changes was found to be directly related to the performance of the patients in a pattern separation task, which might indicate a contribution of various alterations to the mnemonic deficits or the key contribution of other structures to the function.

Discussion

we established for the first time the alterations in both the volume and the microstructure at the level of the hippocampal subfields in a group of unilateral MTLE patients. We observed that these changes are greater in the DG and CA1 at the macrostructural level, and in CA3 and CA1 in the microstructural level. None of these changes had a direct relation to the performance of the patients in a pattern separation task, which suggests a contribution of various alterations to the loss of function.

Multimodal Magnetic Resonance Imaging Depicts Widespread and Subregion Specific Anomalies in the Thalamus of Early-Psychosis and Chronic Schizophrenia Patients

BACKGROUND AND HYPOTHESIS

Although the thalamus has a central role in schizophrenia pathophysiology, contributing to sensory, cognitive, and sleep alterations, the nature and dynamics of the alterations occurring within this structure remain largely elusive. Using a multimodal magnetic resonance imaging (MRI) approach, we examined whether anomalies: (1) differ across thalamic subregions/nuclei, (2) are already present in the early phase of psychosis (EP), and (3) worsen in chronic schizophrenia (SCHZ).

STUDY DESIGN

T1-weighted and diffusion-weighted images were analyzed to estimate gray matter concentration (GMC) and microstructural parameters obtained from the spherical mean technique (intra-neurite volume fraction [VFINTRA)], intra-neurite diffusivity [DIFFINTRA], extra-neurite mean diffusivity [MDEXTRA], extra-neurite transversal diffusivity [TDEXTRA]) within 7 thalamic subregions.

RESULTS

Compared to age-matched controls, the thalamus of EP patients displays previously unreported widespread microstructural alterations (VFINTRA decrease, TDEXTRA increase) that are associated with similar alterations in the whole brain white matter, suggesting altered integrity of white matter fiber tracts in the thalamus. In both patient groups, we also observed more localized and heterogenous changes (either GMC decrease, MDEXTRA increase, or DIFFINTRA decrease) in mediodorsal, posterior, and ventral anterior parts of the thalamus in both patient groups, suggesting that the nature of the alterations varies across subregions. GMC and DIFFINTRA in the whole thalamus correlate with global functioning, while DIFFINTRA in the subregion encompassing the medial pulvinar is significantly associated with negative symptoms in SCHZ.

CONCLUSION

Our data reveals both widespread and more localized thalamic anomalies that are already present in the early phase of psychosis.

ABLE: Automated Brain Lines Extraction Based on Laplacian Surface Collapse

The archetypical folded shape of the human cortex has been a long-standing topic for neuroscientific research. Nevertheless, the accurate neuroanatomical segmentation of sulci remains a challenge. Part of the problem is the uncertainty of where a sulcus transitions into a gyrus and vice versa. This problem can be avoided by focusing on sulcal fundi and gyral crowns, which represent the topological opposites of cortical folding. We present Automated Brain Lines Extraction (ABLE), a method based on Laplacian surface collapse to reliably segment sulcal fundi and gyral crown lines. ABLE is built to work on standard FreeSurfer outputs and eludes the delineation of anastomotic sulci while maintaining sulcal fundi lines that traverse the regions with the highest depth and curvature. First, it segments the cortex into gyral and sulcal surfaces; then, each surface is spatially filtered. A Laplacian-collapse-based algorithm is applied to obtain a thinned representation of the surfaces. This surface is then used for careful detection of the endpoints of the lines. Finally, sulcal fundi and gyral crown lines are obtained by eroding the surfaces while preserving the connectivity between the endpoints. The method is validated by comparing ABLE with three other sulcal extraction methods using the Human Connectome Project (HCP) test-retest database to assess the reproducibility of the different tools. The results confirm ABLE as a reliable method for obtaining sulcal lines with an accurate representation of the sulcal topology while ignoring anastomotic branches and the overestimation of the sulcal fundi lines. ABLE is publicly available via https://github.com/HGGM-LIM/ABLE .

A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain

Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

Depthgram: Visualizing outliers in high-dimensional functional data with application to fMRI data exploration

Functional magnetic resonance imaging (fMRI) is a non‐invasive technique that facilitates the study of brain activity by measuring changes in blood flow. Brain activity signals can be recorded during the alternate performance of given tasks, that is, task fMRI (tfMRI), or during resting‐state, that is, resting‐state fMRI (rsfMRI), as a measure of baseline brain activity. This contributes to the understanding of how the human brain is organized in functionally distinct subdivisions. fMRI experiments from high‐resolution scans provide hundred of thousands of longitudinal signals for each individual, corresponding to brain activity measurements over each voxel of the brain along the duration of the experiment. In this context, we propose novel visualization techniques for high‐dimensional functional data relying on depth‐based notions that enable computationally efficient 2‐dim representations of fMRI data, which elucidate sample composition, outlier presence, and individual variability. We believe that this previous step is crucial to any inferential approach willing to identify neuroscientific patterns across individuals, tasks, and brain regions. We present the proposed technique via an extensive simulation study, and demonstrate its application on a motor and language tfMRI experiment.

Tract-specific damage at spinal cord level in pure hereditary spastic paraplegia type 4: a diffusion tensor imaging study

BACKGROUND

SPG4 is a subtype of hereditary spastic paraplegia (HSP), an upper motor neuron disorder characterized by axonal degeneration of the corticospinal tracts and the fasciculus gracilis. The few neuroimaging studies that have focused on the spinal cord in HSP are based mainly on the analysis of structural characteristics.

METHODS

We assessed diffusion-related characteristics of the spinal cord using diffusion tensor imaging (DTI), as well as structural and shape-related properties in 12 SPG4 patients and 14 controls. We used linear mixed effects models up to T3 in order to analyze the global effects of 'group' and 'clinical data' on structural and diffusion data. For DTI, we carried out a region of interest (ROI) analysis in native space for the whole spinal cord, the anterior and lateral funiculi, and the dorsal columns. We also performed a voxelwise analysis of the spinal cord to study local diffusion-related changes.

RESULTS

A reduced cross-sectional area was observed in the cervical region of SPG4 patients, with significant anteroposterior flattening. DTI analyses revealed significantly decreased fractional anisotropy (FA) and increased radial diffusivity at all the cervical and thoracic levels, particularly in the lateral funiculi and dorsal columns. The FA changes in SPG4 patients were significantly related to disease severity, measured as the Spastic Paraplegia Rating Scale score.

CONCLUSIONS

Our results in SPG4 indicate tract-specific axonal damage at the level of the cervical and thoracic spinal cord. This finding is correlated with the degree of motor disability.

Corticospinal tract and motor cortex degeneration in pure hereditary spastic paraparesis type 4 (SPG4)

Objective: SPG4 is an autosomal dominant pure form of hereditary spastic paraplegia (HSP) caused by mutations in the SPAST gene. HSP is considered an upper motor neuron disorder characterized by progressive retrograde degeneration, or "dying-back" phenomenon, of the corticospinal tract's longest axons. Neuroimaging studies mainly focus on white matter changes and, although previous studies reported cortical thinning in complicated HSP forms, cortical changes remain unclear in SPG4 patients. This work aimed to compare changes in white matter microstructure and cortical thickness between 12 SPG4 patients and 22 healthy age-matched controls. We also explore whether white matter alterations are related to cortical thickness and their correlation with clinical symptoms. Methods: we used fixel-based analysis, an advanced diffusion-weighted imaging technique, and probabilistic tractography of the corticospinal tracts. We also analyzed cortical morphometry using whole-brain surface-based and atlas-based methods in sensorimotor areas. Results: SPG4 patients showed bilateral involvement in the corticospinal tracts; this was more intense in the distal portion than in the upper segments and was associated with the degree of clinical impairment. We found a significant correlation between disease severity and fiber density and cross-section of the corticospinal tracts. Furthermore, corticospinal tract changes were significantly correlated with bilateral cortical thinning in the precentral gyrus in SPG4 patients. Conclusions: Our data point to axonal damage of the corticospinal motor neurons in SPG4 patients might be related to cortical thinning in motor regions.

Thalamic atrophy in patients with pure hereditary spastic paraplegia type 4

SPG4 is an autosomal dominant pure form of hereditary spastic paraplegia (HSP) caused by mutations in the SPAST gene. HSP is considered an upper motor neuron disorder characterized by progressive spasticity and weakness of the lower limbs caused by degeneration of the corticospinal tract. In other neurodegenerative motor disorders, the thalamus and basal ganglia are affected, with a considerable impact on disease progression. However, only a few works have studied these brain structures in HSP, mainly in complex forms of this disease. Our research aims to detect potential alterations in the volume and shape of the thalamus and various basal ganglia structures by comparing 12 patients with pure HSP and 18 healthy controls. We used two neuroimaging procedures: automated segmentation of the subcortical structures (thalamus, hippocampus, caudate nucleus, globus pallidus, and putamen) in native space and shape analysis of the structures. We found a significant reduction in thalamic volume bilaterally, as well as an inward deformation, mainly in the sensory-motor thalamic regions in patients with pure HSP and a mutation in SPG4. We also observed a significant negative correlation between the shape of the thalamus and clinical scores (the Spastic Paraplegia Rating Scale score and disease duration). Moreover, we found a 'Group × Age' interaction that was closely related to the severity of the disease. No differences in volume or in shape were found in the remaining subcortical structures studied. Our results suggest that changes in structure of the thalamus could be an imaging biomarker of disease progression in pHSP.

White Matter Alterations Between Brain Network Hubs Underlie Processing Speed Impairment in Patients With Schizophrenia

Processing speed (PS) impairment is one of the most severe and common cognitive deficits in schizophrenia. Previous studies have reported correlations between PS and white matter diffusion properties, including fractional anisotropy (FA), in several fiber bundles in schizophrenia, suggesting that white matter alterations could underpin decreased PS. In schizophrenia, white matter alterations are most prevalent within inter-hub connections of the rich club. However, the spatial and topological characteristics of this association between PS and FA have not been investigated in patients. In this context, we tested whether structural connections comprising the rich club network would underlie PS impairment in 298 patients with schizophrenia or schizoaffective disorder and 190 healthy controls from the Australian Schizophrenia Research Bank. PS, measured using the digit symbol coding task, was largely (Cohen’s d = 1.33) and significantly (P < .001) reduced in the patient group when compared with healthy controls. Significant associations between PS and FA were widespread in the patient group, involving all cerebral lobes. FA was not associated with other cognitive measures of phonological fluency and verbal working memory in patients, suggesting specificity to PS. A topological analysis revealed that despite being spatially widespread, associations between PS and FA were over-represented among connections forming the rich club network. These findings highlight the need to consider brain network topology when investigating high-order cognitive functions that may be spatially distributed among several brain regions. They also reinforce the evidence that brain hubs and their interconnections may be particularly vulnerable parts of the brain in schizophrenia.

Contributions of left and right anterior temporal lobes to semantic cognition: Evidence from patients with small temporopolar lesions

Decades of research have increased the understanding of the contribution of the anterior temporal lobes (ATLs) to semantic cognition. Nonetheless, whether semantic processing of different types of information show a selective relationship with left and right ATLs, or whether semantic processing in the ATLs is independent of the modality of the input is currently unknown. There exists evidence supporting each of these alternatives. A fundamental objection to these findings is that they were obtained from studies with patients with brain damage affecting extensive regions, sometimes bilaterally. In the current study, we assessed a group of 38 temporal lobe epilepsy (TLE) patients with either left or right small epileptogenic lesions with a battery of commonly used semantic tasks that tested verbal and non-verbal semantic processing. We found that left TLE patients exhibited worse performance than controls on the verbal semantic tasks, as expected, but also on the non-verbal semantic task. On the other hand, performance of the right TLE group did not differ from controls on the non-verbal task, but was worse on a semantic fluency task. When performance between patient groups was compared, we found that left TLE not only did worse than right TLE on the naming task, but also on the non-verbal associative memory task. When considered together, current data do not support a strong view of input modality differences between left and right ATLs. Additionally, they provide evidence indicating that the left and right ATLs do not make similar contributions to a singular functional system for semantic representation.

Geometric renormalization unravels self-similarity of the multiscale human connectome

Structural connectivity in the brain is typically studied by reducing its observation to a single spatial resolution. However, the brain possesses a rich architecture organized over multiple scales linked to one another. We explored the multiscale organization of human connectomes using datasets of healthy subjects reconstructed at five different resolutions. We found that the structure of the human brain remains self-similar when the resolution of observation is progressively decreased by hierarchical coarse-graining of the anatomical regions. Strikingly, a geometric network model, where distances are not Euclidean, predicts the multiscale properties of connectomes, including self-similarity. The model relies on the application of a geometric renormalization protocol which decreases the resolution by coarse-graining and averaging over short similarity distances. Our results suggest that simple organizing principles underlie the multiscale architecture of human structural brain networks, where the same connectivity law dictates short- and long-range connections between different brain regions over many resolutions. The implications are varied and can be substantial for fundamental debates, such as whether the brain is working near a critical point, as well as for applications including advanced tools to simplify the digital reconstruction and simulation of the brain.

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

Morphometric correlates of anomia in patients with small left temporopolar lesions

Visual object naming is a complex cognitive process that engages an interconnected network of cortical regions moving from occipitotemporal to anterior-inferior temporal cortices, and extending into the inferior frontal cortex. Naming can fail for diverse reasons, and different stages of the naming multi-step process appear to be reliant upon the integrity of different neuroanatomical locations. While the neural correlates of semantic errors have been extensively studied, the neural basis of omission errors remains relatively unspecified. Although a strong line of evidence supports an association between anterior temporal lobe damage and semantic errors, there are some studies suggesting that the anterior temporal lobe could be also associated with omissions. However, support for this hypothesis comes from studies with patients in whom damage affected extensive brain regions, sometimes bilaterally. Here, we availed of a group of 12 patients with epilepsy associated with a small lesion at the tip of the left temporal pole. Using an unbiased surface-based morphometry methodology, we correlated two morphological features with errors observed during visual naming. Analyses revealed a correlation between omission errors and reduced local gyrification index in three cortical clusters: one in the left anteromedial temporal lobe region (AMTL) and two in the left anterior cingulate cortex (ACC). Our findings support the view that regions in ACC and AMTL are critical structures within a network engaged in word selection from semantics.

Pregnancy and adolescence entail similar neuroanatomical adaptations: A comparative analysis of cerebral morphometric changes

Mapping the impact of pregnancy on the human brain is essential for understanding the neurobiology of maternal caregiving. Recently, we found that pregnancy leads to a long-lasting reduction in cerebral gray matter volume. However, the morphometric features behind the volumetric reductions remain unexplored. Furthermore, the similarity between these reductions and those occurring during adolescence, another hormonally similar transitional period of life, still needs to be investigated. Here, we used surface-based methods to analyze the longitudinal magnetic resonance imaging data of a group of 25 first-time mothers (before and after pregnancy) and compare them to those of a group of 25 female adolescents (during 2 years of pubertal development). For both first-time mothers and adolescent girls, a monthly rate of volumetric reductions of 0.09 mm³ was observed. In both cases, these reductions were accompanied by decreases in cortical thickness, surface area, local gyrification index, sulcal depth, and sulcal length, as well as increases in sulcal width. In fact, the changes associated with pregnancy did not differ from those that characterize the transition during adolescence in any of these measures. Our findings are consistent with the notion that the brain morphometric changes associated with pregnancy and adolescence reflect similar hormonally primed biological processes.

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging

The thalamic nuclei are involved in many neurodegenerative diseases and therefore, their identification is of key importance in numerous clinical treatments. Automated segmentation of thalamic subparts is currently achieved by exploring diffusion-weighted magnetic resonance imaging (DW-MRI), but in absence of such data, atlas-based segmentation can be used as an alternative. Currently, there is a limited number of available digital atlases of the thalamus. Moreover, all atlases are created using a few subjects only, thus are prone to errors due to the inter-subject variability of the thalamic morphology. In this work, we present a probabilistic atlas of anatomical subparts of the thalamus built upon a relatively large dataset where the individual thalamic parcellation was done by employing a recently proposed automatic diffusion-based clustering method. Our analyses, comparing the segmentation performance between the atlas-based and the clustering method, demonstrate the ability of the provided atlas to substitute the automated diffusion-based subdivision in the individual space when the DW-MRI is not available.

Atypical age-dependency of executive function and white matter microstructure in children and adolescents with autism spectrum disorders

Executive function (EF) performance is associated with measurements of white matter microstructure (WMS) in typical individuals. Impaired EF is a hallmark symptom of autism spectrum disorders (ASD) but it is unclear how impaired EF relates to variability in WMS. Twenty-one male youth (8-18 years) with ASD and without intellectual disability and twenty-one typical male participants (TP) matched for age, intelligence quotient, handedness, race and parental socioeconomic status were recruited. Five EF domains were assessed and several DTI-based measurements of WMS [fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD)] were estimated for eighteen white matter tracts. The ASD group had lower scores for attention (F = 8.37, p = 0.006) and response inhibition (F = 13.09, p = 0.001). Age-dependent changes of EF performance and WMS measurements were present in TP but attenuated in the ASD group. The strongest diagnosis-by-age effect was found for forceps minor, left anterior thalamic radiation and left cingulum angular bundle (all p's ≤ 0.002). In these tracts subjects with ASD tended to have equal or increased FA and/or reduced MD and/or RD at younger ages while controls had increased FA and/or reduced MD and/or RD thereafter. Only for TP individuals, increased FA in the left anterior thalamic radiation was associated with better response inhibition, while reduced RD in forceps minor and left cingulum angular bundle was related to better problem solving and working memory performance respectively. These findings provide novel insight into the age-dependency of EF performance and WMS in ASD, which can be instructive to cognitive training programs.

The Disconnection Hypothesis in Alzheimer's Disease Studied Through Multimodal Magnetic Resonance Imaging: Structural, Perfusion, and Diffusion Tensor Imaging

According to the so-called disconnection hypothesis, the loss of synaptic inputs from the medial temporal lobes (MTL) in Alzheimer's disease (AD) may lead to reduced activity of target neurons in cortical areas and, consequently, to decreased cerebral blood flow (CBF) in those areas. The aim of this study was to assess whether hypoperfusion in parietotemporal and frontal cortices of patients with mild cognitive impairment who converted to AD (MCI-c) and patients with mild AD is associated with atrophy in the MTL and/or microstructural changes in the white matter (WM) tracts connecting these areas. We assessed these relationships by investigating correlations between CBF in hypoperfused areas, mean cortical thickness in atrophied regions of the MTL, and fractional anisotropy (FA) in WM tracts. In the MCI-c group, a strong correlation was observed between CBF of the superior parietal gyri and FA in the parahippocampal tracts (left: r = 0.90, p <  0.0001; right: r = 0.597, p = 0.024), and between FA in the right parahippocampal tract and the right precuneus (r = 0.551, p = 0.041). No significant correlations between CBF in hypoperfused regions and FA in the WM tract were observed in the AD group. These results suggest an association between perfusion deficits and altered WM tracts in prodromal AD, while microvasculature impairments may have a greater influence in more advanced stages. We did not find correlations between cortical thinning in the medial temporal lobes and decreased FA in the WM tracts of the limbic system in either group.

Cortical morphometry in frontoparietal and default mode networks in math-gifted adolescents

Math-gifted subjects are characterized by above-age performance in intelligence tests, exceptional creativity, and high task commitment. Neuroimaging studies reveal enhanced functional brain organization and white matter microstructure in the frontoparietal executive network of math-gifted individuals. However, the cortical morphometry of these subjects remains largely unknown. The main goal of this study was to compare the cortical morphometry of math-gifted adolescents with that of an age- and IQ-matched control group. We used surface-based methods to perform a vertex-wise analysis of cortical thickness and surface area. Our results show that math-gifted adolescents present a thinner cortex and a larger surface area in key regions of the frontoparietal and default mode networks, which are involved in executive processing and creative thinking, respectively. The combination of reduced cortical thickness and larger surface area suggests above-age neural maturation of these networks in math-gifted individuals. Hum Brain Mapp 37:1893-1902, 2016. © 2016 Wiley Periodicals, Inc.

Spherical Deconvolution of Multichannel Diffusion MRI Data with Non-Gaussian Noise Models and Spatial Regularization

Spherical deconvolution (SD) methods are widely used to estimate the intra-voxel white-matter fiber orientations from diffusion MRI data. However, while some of these methods assume a zero-mean Gaussian distribution for the underlying noise, its real distribution is known to be non-Gaussian and to depend on many factors such as the number of coils and the methodology used to combine multichannel MRI signals. Indeed, the two prevailing methods for multichannel signal combination lead to noise patterns better described by Rician and noncentral Chi distributions. Here we develop a Robust and Unbiased Model-BAsed Spherical Deconvolution (RUMBA-SD) technique, intended to deal with realistic MRI noise, based on a Richardson-Lucy (RL) algorithm adapted to Rician and noncentral Chi likelihood models. To quantify the benefits of using proper noise models, RUMBA-SD was compared with dRL-SD, a well-established method based on the RL algorithm for Gaussian noise. Another aim of the study was to quantify the impact of including a total variation (TV) spatial regularization term in the estimation framework. To do this, we developed TV spatially-regularized versions of both RUMBA-SD and dRL-SD algorithms. The evaluation was performed by comparing various quality metrics on 132 three-dimensional synthetic phantoms involving different inter-fiber angles and volume fractions, which were contaminated with noise mimicking patterns generated by data processing in multichannel scanners. The results demonstrate that the inclusion of proper likelihood models leads to an increased ability to resolve fiber crossings with smaller inter-fiber angles and to better detect non-dominant fibers. The inclusion of TV regularization dramatically improved the resolution power of both techniques. The above findings were also verified in human brain data.

fMRat: an extension of SPM for a fully automatic analysis of rodent brain functional magnetic resonance series

The purpose of this study was to develop a multi-platform automatic software tool for full processing of fMRI rodent studies. Existing tools require the usage of several different plug-ins, a significant user interaction and/or programming skills. Based on a user-friendly interface, the tool provides statistical parametric brain maps (t and Z) and percentage of signal change for user-provided regions of interest. The tool is coded in MATLAB (MathWorks(®)) and implemented as a plug-in for SPM (Statistical Parametric Mapping, the Wellcome Trust Centre for Neuroimaging). The automatic pipeline loads default parameters that are appropriate for preclinical studies and processes multiple subjects in batch mode (from images in either Nifti or raw Bruker format). In advanced mode, all processing steps can be selected or deselected and executed independently. Processing parameters and workflow were optimized for rat studies and assessed using 460 male-rat fMRI series on which we tested five smoothing kernel sizes and three different hemodynamic models. A smoothing kernel of FWHM = 1.2 mm (four times the voxel size) yielded the highest t values at the somatosensorial primary cortex, and a boxcar response function provided the lowest residual variance after fitting. fMRat offers the features of a thorough SPM-based analysis combined with the functionality of several SPM extensions in a single automatic pipeline with a user-friendly interface. The code and sample images can be downloaded from https://github.com/HGGM-LIM/fmrat .

Cortical morphology of adolescents with bipolar disorder and with schizophrenia

INTRODUCTION

Recent evidence points to overlapping decreases in cortical thickness and gyrification in the frontal lobe of patients with adult-onset schizophrenia and bipolar disorder with psychotic symptoms, but it is not clear if these findings generalize to patients with a disease onset during adolescence and what may be the mechanisms underlying a decrease in gyrification.

METHOD

This study analyzed cortical morphology using surface-based morphometry in 92 subjects (age range 11-18 years, 52 healthy controls and 40 adolescents with early-onset first-episode psychosis diagnosed with schizophrenia (n=20) or bipolar disorder with psychotic symptoms (n=20) based on a two year clinical follow up). Average lobar cortical thickness, surface area, gyrification index (GI) and sulcal width were compared between groups, and the relationship between the GI and sulcal width was assessed in the patient group.

RESULTS

Both patients groups showed decreased cortical thickness and increased sulcal width in the frontal cortex when compared to healthy controls. The schizophrenia subgroup also had increased sulcal width in all other lobes. In the frontal cortex of the combined patient group sulcal width was negatively correlated (r=-0.58, p<0.001) with the GI.

CONCLUSIONS

In adolescents with schizophrenia and bipolar disorder with psychotic symptoms there is cortical thinning, decreased GI and increased sulcal width of the frontal cortex present at the time of the first psychotic episode. Decreased frontal GI is associated with the widening of the frontal sulci which may reduce sulcal surface area. These results suggest that abnormal growth (or more pronounced shrinkage during adolescence) of the frontal cortex represents a shared endophenotype for psychosis.

Brain morphometry of Dravet syndrome

The aim of this study was to identify differential global and local brain structural patterns in Dravet Syndrome (DS) patients as compared with a control subject group, using brain morphometry techniques which provide a quantitative whole-brain structural analysis that allows for specific patterns to be generalized across series of individuals. Nine patients with the diagnosis of DS that tested positive for mutation in the SCN1A gene and nine well-matched healthy controls were investigated using voxel brain morphometry (VBM), cortical thickness and cortical gyrification measurements. Global volume reductions of gray matter (GM) and white matter (WM) were related to DS. Local volume reductions corresponding to several white matter regions in brainstem, cerebellum, corpus callosum, corticospinal tracts and association fibers (left inferior fronto-occipital fasciculus and left uncinate fasciculus) were also found. Furthermore, DS showed a reduced cortical folding in the right precentral gyrus. The present findings describe DS-related brain structure abnormalities probably linked to the expression of the SCN1A mutation.

Is the cerebellum the optimal reference region for intensity normalization of perfusion MR studies in early Alzheimer's disease?

The cerebellum is the region most commonly used as a reference when normalizing the intensity of perfusion images acquired using magnetic resonance imaging (MRI) in Alzheimer's disease (AD) studies. In addition, the cerebellum provides unbiased estimations with nuclear medicine techniques. However, no reports confirm the cerebellum as an optimal reference region in MRI studies or evaluate the consequences of using different normalization regions. In this study, we address the effect of using the cerebellum, whole-brain white matter, and whole-brain cortical gray matter in the normalization of cerebral blood flow (CBF) parametric maps by comparing patients with stable mild cognitive impairment (MCI), patients with AD and healthy controls. According to our results, normalization by whole-brain cortical gray matter enables more sensitive detection of perfusion abnormalities in AD patients and reveals a larger number of affected regions than data normalized by the cerebellum or whole-brain white matter. Therefore, the cerebellum is not the most valid reference region in MRI studies for early stages of AD. After normalization by whole-brain cortical gray matter, we found a significant decrease in CBF in both parietal lobes and an increase in CBF in the right medial temporal lobe. We found no differences in perfusion between patients with stable MCI and healthy controls either before or after normalization.

White matter microstructure correlates of mathematical giftedness and intelligence quotient

Recent functional neuroimaging studies have shown differences in brain activation between mathematically gifted adolescents and controls. The aim of this study was to investigate the relationship between mathematical giftedness, intelligent quotient (IQ), and the microstructure of white matter tracts in a sample composed of math-gifted adolescents and aged-matched controls. Math-gifted subjects were selected through a national program based on detecting enhanced visuospatial abilities and creative thinking. We used diffusion tensor imaging to assess white matter microstructure in neuroanatomical connectivity. The processing included voxel-wise and region of interest-based analyses of the fractional anisotropy (FA), a parameter which is purportedly related to white matter microstructure. In a whole-sample analysis, IQ showed a significant positive correlation with FA, mainly in the corpus callosum, supporting the idea that efficient information transfer between hemispheres is crucial for higher intellectual capabilities. In addition, math-gifted adolescents showed increased FA (adjusted for IQ) in white matter tracts connecting frontal lobes with basal ganglia and parietal regions. The enhanced anatomical connectivity observed in the forceps minor and splenium may underlie the greater fluid reasoning, visuospatial working memory, and creative capabilities of these children.

Regional specificity of thalamic volume deficits in male adolescents with early-onset psychosis

BACKGROUND

Thalamic volume deficits are associated with psychosis but it is unclear whether the volume reduction is uniformly distributed or whether it is more severe in particular thalamic regions.

AIMS

To quantify whole and regional thalamic volume in males with early-onset psychosis and healthy male controls.

METHOD

Brain scans were obtained for 80 adolescents: 46 individuals with early-onset psychosis with a duration of positive symptoms less than 6 months and 34 healthy controls. All participants were younger than 19 years. Total thalamic volumes were assessed using FreeSurfer and FSL-FIRST, group comparisons of regional thalamic volumes were studied with a surface-based approach.

RESULTS

Total thalamic volume was smaller in participants with early-onset psychosis relative to controls. Regional thalamic volume reduction was most significant in the right anterior mediodorsal area and pulvinar.

CONCLUSIONS

In males with minimally treated early-onset psychosis, thalamic volume deficits may be most pronounced in the anterior mediodorsal and posterior pulvinar regions, adding strength to findings from post-mortem studies in adults with psychosis.

Surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks

Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.

Approximate average head models for EEG source imaging

We examine the performance of approximate models (AM) of the head in solving the EEG inverse problem. The AM are needed when the individual's MRI is not available. We simulate the electric potential distribution generated by cortical sources for a large sample of 305 subjects, and solve the inverse problem with AM. Statistical comparisons are carried out with the distribution of the localization errors. We propose several new AM. These are the average of many individual realistic MRI-based models, such as surface-based models or lead fields. We demonstrate that the lead fields of the AM should be calculated considering source moments not constrained to be normal to the cortex. We also show that the imperfect anatomical correspondence between all cortices is the most important cause of localization errors. Our average models perform better than a random individual model or the usual average model in the MNI space. We also show that a classification based on race and gender or head size before averaging does not significantly improve the results. Our average models are slightly better than an existing AM with shape guided by measured individual electrode positions, and have the advantage of not requiring such measurements. Among the studied models, the Average Lead Field seems the most convenient tool in large and systematical clinical and research studies demanding EEG source localization, when MRI are unavailable. This AM does not need a strict alignment between head models, and can therefore be easily achieved for any type of head modeling approach.

Inferring multiple maxima in intravoxel white matter fiber distribution

A new methodology is introduced that characterizes the intravoxel orientation distribution function (ODF) based on a single-fiber model of the diffusion MRI signal. Using a Bayesian framework the probability of finding a fiber in a specific orientation is obtained. The proposed ODF estimation relies on a cigar-like diffusion tensor model, the methodology is thus denominated Bayesian cigar-like diffusion tensor (BCDT). This work makes two major contributions: 1) the study of single-fiber models in detecting fibers with different volume fractions in a voxel, and 2) the introduction of the Nth-root correction to improve the detection of fibers with smaller volume fractions, where N is the number of diffusion MRI measurements. It is demonstrated that the incomplete signal modeling fails to reconstruct the relative fiber volume fractions, especially when the intravoxel diffusion profiles have dissimilar contributions to the diffusion MRI signal. In this situation the fibers with smaller contributions are hardly detectable. The BCDT method proposed here reduces this effect by introducing the Nth-root correction, making multiple fibers estimable. The performance of the new methodology is illustrated using synthetic and real data, as well as the data from a phantom of intersecting capillaries.

A Bayesian framework to identify principal intravoxel diffusion profiles based on diffusion-weighted MR imaging

In this paper we introduce a new method to characterize the intravoxel anisotropy based on diffusion-weighted imaging (DWI). The proposed solution, under a fully Bayesian formalism, deals with the problem of joint Bayesian Model selection and parameter estimation to reconstruct the principal diffusion profiles or primary fiber orientations in a voxel. We develop an efficient stochastic algorithm based on the reversible jump Markov chain Monte Carlo (RJMCMC) method in order to perform the Bayesian computation. RJMCMC is a good choice for this problem because of its ability to jump between models of different dimensionality. This methodology provides posterior estimates of the parameters of interest (fiber orientation, diffusivities etc) unconditional of the model assumed. It also gives an empirical posterior distribution of the number of primary nerve fiber orientations given the DWI data. Different probability maps can be assessed using this methodology: 1) the intravoxel fiber orientation map (or orientational distribution function) that gives the probability of finding a fiber in a particular spatial orientation; 2) a three-dimensional map of the probability of finding a particular number of fibers in each voxel; 3) a three-dimensional MaxPro (maximum probability) map that provides the most probable number of fibers for each voxel. In order to study the performance and reliability of the presented approach, we tested it on synthetic data; an ex-vivo phantom of intersecting capillaries; and DWI data from a human subject.

Characterizing brain anatomical connections using diffusion weighted MRI and graph theory

A new methodology based on Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) and Graph Theory is presented for characterizing the anatomical connections between brain gray matter areas. In a first step, brain voxels are modeled as nodes of a non-directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers. This probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function, obtained from anatomical MRI and DW-MRI, respectively. A new tractography algorithm for finding white matter routes is also introduced. This algorithm solves the most probable path problem between any two nodes, leading to the assessment of probabilistic brain anatomical connection maps. In a second step, for assessing anatomical connectivity between K gray matter structures, the previous graph is redefined as a K+1 partite graph by partitioning the initial nodes set in K non-overlapped gray matter subsets and one subset clustering the remaining nodes. Three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets: Anatomical Connection Strength (ACS), Anatomical Connection Density (ACD) and Anatomical Connection Probability (ACP). This methodology was applied to both artificial and actual human data. Results show that nervous fiber pathways between some regions of interest were reconstructed correctly. Additionally, mean connectivity maps of ACS, ACD and ACP between 71 gray matter structures for five healthy subjects are presented.