Evaluating accuracy of striatal, pallidal, and thalamic segmentation methods: Comparing automated approaches to manual delineation

The effects of psychotherapy for anhedonia on subcortical brain volumes measured with ultra-high field MRI

BACKGROUND

Anhedonia is a transdiagnostic symptom often resistant to treatment. The identification of biomarkers sensitive to anhedonia treatment will aid in the evaluation of novel anhedonia interventions.

METHODS

This is an exploratory analysis of changes in subcortical brain volumes accompanying psychotherapy in a transdiagnostic anhedonic sample using ultra-high field (7-Tesla) MRI. Outpatients with clinically impairing anhedonia (n = 116) received Behavioral Activation Treatment for Anhedonia, a novel psychotherapy, or Mindfulness-Based Cognitive Therapy (ClinicalTrials.gov Identifiers NCT02874534 and NCT04036136). Subcortical brain volumes were estimated via the MultisegPipeline, and regions of interest were the amygdala, caudate nucleus, hippocampus, pallidum, putamen, and thalamus. Bivariate mixed effects models estimated pre-treatment relations between anhedonia severity and subcortical brain volumes, change over time in subcortical brain volumes, and associations between changes in subcortical brain volumes and changes in anhedonia symptoms.

RESULTS

As reported previously (Cernasov et al., 2023), both forms of psychotherapy resulted in equivalent and significant reductions in anhedonia symptoms. Pre-treatment anhedonia severity and subcortical brain volumes were not related. No changes in subcortical brain volumes were observed over the course of treatment. Additionally, no relations were observed between changes in subcortical brain volumes and changes in anhedonia severity over the course of treatment.

LIMITATIONS

This trial included a modest sample size and did not have a waitlist-control condition or a non-anhedonic comparison group.

CONCLUSIONS

In this exploratory analysis, psychotherapy for anhedonia was not accompanied by changes in subcortical brain volumes, suggesting that subcortical brain volumes may not be a candidate biomarker sensitive to response to psychotherapy.

CT-guided spatial normalization of nuclear hybrid imaging adapted to enlarged ventricles: Impact on striatal uptake quantification

INTRODUCTION

Spatial normalization is a prerequisite step for the quantitative analysis of SPECT or PET brain images using volume-of-interest (VOI) template or voxel-based analysis. MRI-guided spatial normalization is the gold standard, but the wide use of PET/CT or SPECT/CT in routine clinical practice makes CT-guided spatial normalization a necessary alternative. Ventricular enlargement is observed with aging, and it hampers the spatial normalization of the lateral ventricles and striatal regions, limiting their analysis. The aim of the present study was to propose a robust spatial normalization method based on CT scans that takes into account features of the aging brain to reduce bias in the CT-guided striatal analysis of SPECT images.

METHODS

We propose an enhanced CT-guided spatial normalization pipeline based on SPM12. Performance of the proposed pipeline was assessed on visually normal [123I]-FP-CIT SPECT/CT images. SPM12 default CT-guided spatial normalization was used as reference method. The metrics assessed were the overlap between the spatially normalized lateral ventricles and caudate/putamen VOIs, and the computation of caudate and putamen specific binding ratios (SBR).

RESULTS

In total 231 subjects (mean age ± SD = 61.9 ± 15.5 years) were included in the statistical analysis. The mean overlap between the spatially normalized lateral ventricles of subjects and the caudate VOI and the mean SBR of caudate were respectively 38.40 % (± SD = 19.48 %) of the VOI and 1.77 (± 0.79) when performing SPM12 default spatial normalization. The mean overlap decreased to 9.13 % (± SD = 1.41 %, P < 0.001) of the VOI and the SBR of caudate increased to 2.38 (± 0.51, P < 0.0001) when performing the proposed pipeline. Spatially normalized lateral ventricles did not overlap with putamen VOI using either method. The mean putamen SBR value derived from the proposed spatial normalization (2.75 ± 0.54) was not significantly different from that derived from the default SPM12 spatial normalization (2.83 ± 0.52, P > 0.05).

CONCLUSION

The automatic CT-guided spatial normalization used herein led to a less biased spatial normalization of SPECT images, hence an improved semi-quantitative analysis. The proposed pipeline could be implemented in clinical routine to perform a more robust SBR computation using hybrid imaging.

Perinatal maternal mental health and amygdala morphology in young adulthood

The pre- and perinatal environment is thought to play a critical role in shaping brain development. Specifically, maternal mental health and maternal care have been shown to influence offspring brain development in regions implicated in emotional regulation such as the amygdala. In this study, we used data from a neuroimaging follow-up of a prenatal birth-cohort, the European Longitudinal Study of Pregnancy and Childhood, to investigate the impact of early postnatal maternal anxiety/co-dependence, and prenatal and early-postnatal depression and dysregulated mood on amygdala volume and morphology in young adulthood (n = 103). We observed that in typically developing young adults, greater maternal anxiety/co-dependence after birth was significantly associated with lower volume (right: t = -2.913, p = 0.0045, β = -0.523; left: t = -1.471, p = 0.144, β = -0.248) and non-significantly associated with surface area (right: t = -3.502, q = 0.069, <10%FDR, β = -0.090, left: t = -3.137, q = 0.117, <10%FDR, = -0.088) of the amygdala in young adulthood. Conversely, prenatal maternal depression and mood dysregulation in the early postnatal period was not associated with any volumetric or morphological changes in the amygdala in young adulthood. Our findings provide evidence for subtle but long-lasting alterations to amygdala morphology associated with differences in maternal anxiety/co-dependence in early development.

Multisite test-retest reliability and compatibility of brain metrics derived from FreeSurfer versions 7.1, 6.0, and 5.3

Automatic neuroimaging processing tools provide convenient and systematic methods for extracting features from brain magnetic resonance imaging scans. One tool, FreeSurfer, provides an easy-to-use pipeline to extract cortical and subcortical morphometric measures. There have been over 25 stable releases of FreeSurfer, with different versions used across published works. The reliability and compatibility of regional morphometric metrics derived from the most recent version releases have yet to be empirically assessed. Here, we used test-retest data from three public data sets to determine within-version reliability and between-version compatibility across 42 regional outputs from FreeSurfer versions 7.1, 6.0, and 5.3. Cortical thickness from v7.1 was less compatible with that of older versions, particularly along the cingulate gyrus, where the lowest version compatibility was observed (intraclass correlation coefficient 0.37-0.61). Surface area of the temporal pole, frontal pole, and medial orbitofrontal cortex, also showed low to moderate version compatibility. We confirm low compatibility between v6.0 and v5.3 of pallidum and putamen volumes, while those from v7.1 were compatible with v6.0. Replication in an independent sample showed largely similar results for measures of surface area and subcortical volumes, but had lower overall regional thickness reliability and compatibility. Batch effect correction may adjust for some inter-version effects when most sites are run with one version, but results vary when more sites are run with different versions. Age associations in a quality controlled independent sample (N = 106) revealed version differences in results of downstream statistical analysis. We provide a reference to highlight the regional metrics that may yield recent version-related inconsistencies in published findings. An interactive viewer is provided at http://data.brainescience.org/Freesurfer_Reliability/.

Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis

Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.

Anatomically curated segmentation of human subcortical structures in high resolution magnetic resonance imaging: An open science approach

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

DBB - A Distorted Brain Benchmark for Automatic Tissue Segmentation in Paediatric Patients

T1-weighted magnetic resonance images provide a comprehensive view of the morphology of the human brain at the macro scale. These images are usually the input of a segmentation process that aims detecting the anatomical structures labeling them according to a predefined set of target tissues. Automated methods for brain tissue segmentation rely on anatomical priors of the human brain structures. This is the reason why their performance is quite accurate on healthy individuals. Nevertheless model-based tools become less accurate in clinical practice, specifically in the cases of severe lesions or highly distorted cerebral anatomy. More recently there are empirical evidences that a data-driven approach can be more robust in presence of alterations of brain structures, even though the learning model is trained on healthy brains. Our contribution is a benchmark to support an open investigation on how the tissue segmentation of distorted brains can be improved by adopting a supervised learning approach. We formulate a precise definition of the task and propose an evaluation metric for a fair and quantitative comparison. The training sample is composed of almost one thousand healthy individuals. Data include both T1-weighted MR images and their labeling of brain tissues. The test sample is a collection of several tens of individuals with severe brain distortions. Data and code are openly published on BrainLife, an open science platform for reproducible neuroscience data analysis.

Medial temporal lobe and basal ganglia volume trajectories in persistent negative symptoms following a first episode of psychosis

BACKGROUND

Persistent negative symptoms (PNS, e.g., avolition, anhedonia, alogia) are present in up to 30% of individuals diagnosed with a first episode of psychosis and greatly impact functional outcomes. PNS and secondary PNS (sPNS: concomitant with positive, depressive, or extrapyramidal symptoms) may index distinct pathophysiologies reflected by structural brain changes, particularly in the medial temporal lobe (MTL) and basal ganglia.

AIMS

We sought to characterize dynamic brain changes related to PNS over the course of 2 years following a first episode of psychosis.

METHOD

Longitudinal volumetric trajectories within the MTL (hippocampus, parahippocampal gyrus, entorhinal cortex, perirhinal cortex) and basal ganglia (caudate, putamen, pallidum) were investigated in 98 patients with first-episode psychosis and 86 healthy controls using generalized estimating equations.

RESULTS

In left hippocampus, PNS (n = 25 at baseline) showed decreased volumes over time, sPNS (n = 26) volumes remained stable, and non-PNS (n = 47) volumes increased over time to control levels. PNS-specific changes were observed in left hippocampus and left perirhinal cortex, with the greatest decline from 12 to 24 months to levels significantly below those of non-PNS and controls. Affective/non-affective diagnosis, antipsychotic medication dosage and adherence at baseline did not significantly impact these findings. Basal ganglia volume trajectories did not distinguish between PNS and sPNS.

CONCLUSIONS

The current study highlights distinct structural brain trajectories in PNS that are prominent in the left MTL. Basal ganglia alterations may contribute to PNS irrespective of their etiology. Left MTL volume reductions were most evident after 1 year of treatment, highlighting the importance of targeted early interventions.

Subcortical and hippocampal brain segmentation in 5-year-old children: validation of FSL-FIRST and FreeSurfer against manual segmentation

Developing accurate subcortical volumetric quantification tools is crucial for neurodevelopmental studies, as they could reduce the need for challenging and time‐consuming manual segmentation. In this study, the accuracy of two automated segmentation tools, FSL‐FIRST (with three different boundary correction settings) and FreeSurfer, were compared against manual segmentation of the hippocampus and subcortical nuclei, including the amygdala, thalamus, putamen, globus pallidus, caudate and nucleus accumbens, using volumetric and correlation analyses in 80 5‐year‐olds.

Both FSL‐FIRST and FreeSurfer overestimated the volume on all structures except the caudate, and the accuracy varied depending on the structure. Small structures such as the amygdala and nucleus accumbens, which are visually difficult to distinguish, produced significant overestimations and weaker correlations with all automated methods. Larger and more readily distinguishable structures such as the caudate and putamen produced notably lower overestimations and stronger correlations. Overall, the segmentations performed by FSL‐FIRST's default pipeline were the most accurate, whereas FreeSurfer's results were weaker across the structures.

In line with prior studies, the accuracy of automated segmentation tools was imperfect with respect to manually defined structures. However, apart from amygdala and nucleus accumbens, FSL‐FIRST's agreement could be considered satisfactory (Pearson correlation > 0.74, intraclass correlation coefficient (ICC) > 0.68 and Dice score coefficient (DSC) > 0.87) with highest values for the striatal structures (putamen, globus pallidus, caudate) (Pearson correlation > 0.77, ICC > 0.87 and DSC > 0.88, respectively). Overall, automated segmentation tools do not always provide satisfactory results, and careful visual inspection of the automated segmentations is strongly advised.

Thalamic and striato-pallidal volumes in schizophrenia patients and individuals at risk for psychosis: A multi-atlas segmentation study

Despite previous neuroimaging studies demonstrating morphological abnormalities of the thalamus and other subcortical structures in patients with schizophrenia, the potential role of the thalamus and its subdivisions in the pathophysiology of this illness remains elusive. It is also unclear whether similar changes of these structures occur in individuals at high risk for psychosis. In this study, magnetic resonance imaging was employed with the Multiple Automatically Generated Templates (MAGeT) brain segmentation algorithm to determine volumes of the thalamic subdivisions, the striatum (caudate, putamen, and nucleus accumbens), and the globus pallidus in 62 patients with schizophrenia, 38 individuals with an at-risk mental state (ARMS) [4 of whom (10.5%) subsequently developed schizophrenia], and 61 healthy subjects. Cognitive function of the patients was assessed by using the Brief Assessment of Cognition in Schizophrenia (BACS) and the Schizophrenia Cognition Rating Scale (SCoRS). Thalamic volume (particularly the medial dorsal and ventral lateral nuclei) was smaller in the schizophrenia group than the ARMS and control groups, while there were no differences for the striatum and globus pallidus. In the schizophrenia group, the reduction of thalamic ventral lateral nucleus volume was significantly associated with lower BACS score. The pallidal volume was positively correlated with the dose of antipsychotic treatment in the schizophrenia group. These results suggest that patients with schizophrenia, but not those with ARMS, exhibit volume reduction in specific thalamic subdivisions, which may underlie core clinical features of this illness.

A Comparative Analysis of MRI Automated Segmentation of Subcortical Brain Volumes in a Large Dataset of Elderly Subjects

In this study, we perform a comparative analysis of automated image segmentation of subcortical structures in the elderly brain. Manual segmentation is very time-consuming and automated methods are gaining importance as a clinical tool for diagnosis. The two most commonly used software libraries for brain segmentation -FreeSurfer and FSL- are put to work in a large dataset of 4,028 magnetic resonance imaging (MRI) scans collected for this study. We find a lack of linear correlation between the segmentation volume estimates obtained from FreeSurfer and FSL. On the other hand, FreeSurfer volume estimates tend to be larger thanFSL estimates of the areas putamen, thalamus, amygdala, caudate, pallidum, hippocampus, and accumbens. The characterization of the performance of brain segmentation algorithms in large datasets as the one presented here is a necessary step towards partially or fully automated end-to-end neuroimaging workflow both in clinical and research settings.

Multitask Learning Based Three-Dimensional Striatal Segmentation of MRI: fMRI and PET Objective Assessments

BACKGROUND

Recent studies have established a clear topographical and functional organization of projections to and from complex subdivisions of the striatum. Manual segmentation of these functional subdivisions is labor-intensive and time-consuming, and automated methods are not as reliable as manual segmentation.

PURPOSE

To utilize multitask learning (MTL) as a method to segment subregions of the striatum consisting of pre-commissural putamen (prePU), pre-commissural caudate (preCA), post-commissural putamen (postPU), post-commissural caudate (postCA), and ventral striatum (VST).

STUDY TYPE

Retrospective.

POPULATION

Eighty-seven total data sets from patients with schizophrenia and matched controls.

FIELD STRENGTH/SEQUENCE

1.5 T and 3.0 T, T1 -weighted (SPGR SENSE, 3D BRAVO).

ASSESSMENT

MTL-generated segmentations were compared to the Imperial College London Clinical Imaging Center (CIC) atlas. Dice similarity coefficient (DSC) was used to compare the automated methods to manual segmentations. Positron emission tomography (PET) imaging: 60 minutes of emission data were acquired using [11 C]raclopride. Data were reconstructed by filtered back projection (FBP) with computed tomography (CT) used for attenuation correction. Binding potential values, BPND , and region of interest (ROI) time series and whole-brain connectivity using functional magnetic resonance imaging (fMRI) images were compared between manual and both automated segmentations.

STATISTICAL TESTS

Pearson correlation and paired t-test.

RESULTS

MTL-generated segmentations showed excellent spatial agreement with manual (DSC ≥0.72 across all striatal subregions). BPND values from MTL-generated segmentations were shown to correlate well with manual segmentations with R2  ≥ 0.91 in all caudate and putamen subregions, and R2  = 0.69 in VST. Mean Pearson correlation coefficients of the fMRI data between MTL-generated and manual segmentations were also high in time series (≥0.86) and whole-brain connectivity (≥0.89) across all subregions.

DATA CONCLUSION

Across both PET and fMRI task-based assessments, results from MTL-generated segmentations more closely corresponded to results from manually drawn ROIs than CIC-generated segmentations did. Therefore, the proposed MTL approach is a fast and reliable method for three-dimensional striatal subregion segmentation with results comparable to manually segmented ROIs.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY STAGE: 1.

Involvement of the habenula in the pathophysiology of autism spectrum disorder

The habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6-30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.

A Volumetric Study of the Corpus Callosum in the Turkish Population

Objective The aim of this study is to measure the average corpus callosum (CC) volume of healthy Turkish humans and to analyze the effects of gender and age on volumes, including the genu, truncus, and splenium parts of the CC.

Patients and Methods Magnetic resonance imaging brain scans were obtained from 301 healthy male and female subjects, aged 11 to 84 years. The median age was 42 years (min–max: 11–82) in females and 49 years (min–max: 12–84) in males. Corpus callosum and its parts were calculated by using MRICloud. CC volumes of each subject were compared with those of the age and gender groups.

Results All volumes of the CC were significantly higher in males than females. All left volumes except BCC were significantly higher than the right volumes in both males and females. The oldest two age groups (50–69 and 70–84 years) were found to have higher bilateral CC volumes, and bilateral BCC volumes were also higher than in the other two age groups (11–29 and 30–49 years).

Conclusion The results suggest that compared with females/males, females have a faster decline in the volume of all volumes of the CC. We think that quantitative structural magnetic resonance data of the brain is vital in understanding human brain function and development.

The impact of the Siemens Tim Trio to Prisma upgrade and the addition of volumetric navigators on cortical thickness, structure volume, and 1H-MRS indices: An MRI reliability study with implications for longitudinal study designs

Many magnetic resonance imaging (MRI) measures are being studied longitudinally to explore topics such as biomarker detection and clinical staging. A pertinent concern to longitudinal work is MRI scanner upgrades. When upgrades occur during the course of a longitudinal MRI neuroimaging investigation, there may be an impact on the compatibility of pre- and post-upgrade measures. Similarly, subject motion is another issue that may be detrimental to MRI work and embedding volumetric navigators (vNavs) within acquisition sequences has emerged as a technique that allows for prospective motion correction. Our research group recently underwent an upgrade from a Siemens MAGNETOM 3T Tim Trio system to a Siemens MAGNETOM 3T Prisma Fit system. The goals of the current work were to: 1) investigate the impact of this upgrade on commonly used structural imaging measures and proton magnetic resonance spectroscopy indices ("Prisma Upgrade protocol") and 2) examine structural imaging measures in a sequence with vNavs alongside a standard acquisition sequence ("vNav protocol"). While high reliability was observed for most of the investigated MRI outputs, suboptimal reliability was observed for certain indices. Across the scanner upgrade, increases in frontal, temporal, and cingulate cortical thickness (CT) and thalamus volume, along with decreases in parietal CT and amygdala, globus pallidus, hippocampus, and striatum volumes, were observed. No significant impact of the upgrade was found in ¹H-MRS analyses. Further, CT estimates were found to be larger in MPRAGE acquisitions compared to vNav-MPRAGE acquisitions mainly within temporal areas, while the opposite was found mostly in parietal brain regions. The results from this work should be considered in longitudinal study designs and comparable prospective motion correction investigations are warranted in cases of marked head movement.

FreeSurfer 5.3 versus 6.0: are volumes comparable? A Chronic Effects of Neurotrauma Consortium study

Automated neuroimaging methods like FreeSurfer ( https://surfer.nmr.mgh.harvard.edu/ ) have revolutionized quantitative neuroimaging analyses. Such analyses provide a variety of metrics used for image quantification, including magnetic resonance imaging (MRI) volumetrics. With the release of FreeSurfer version 6.0, it is important to assess its comparability to the widely-used previous version 5.3. The current study used data from the initial 249 participants in the ongoing Chronic Effects of Neurotrauma Consortium (CENC) multicenter observational study to compare the volumetric output of versions 5.3 and 6.0 across various regions of interest (ROI). In the current investigation, the following ROIs were examined: total intracranial volume, total white matter volume, total ventricular volume, total gray matter volume, and right and left volumes for the thalamus, pallidum, putamen, caudate, amygdala and hippocampus. Absolute ROI volumes derived from FreeSurfer 6.0 differed significantly from those obtained using version 5.3. We also employed a clinically-based evaluation strategy to compare both versions in their prediction of age-mediated volume reductions (or ventricular increase) in the aforementioned structures. Statistical comparison involved both general linear modeling (GLM) and random forest (RF) methods, where cross-validation error was significantly higher using segmentations from FreeSurfer version 5.3 versus version 6.0 (GLM: t = 4.97, df = 99, p value = 2.706e-06; RF: t = 4.85, df = 99, p value = 4.424e-06). Additionally, the relative importance of ROIs used to predict age using RFs differed between FreeSurfer versions, indicating substantial differences in the two versions. However, from the perspective of correlational analyses, fitted regression lines and their slopes were similar between the two versions, regardless of version used. While absolute volumes are not interchangeable between version 5.3 and 6.0, ROI correlational analyses appear to yield similar results, suggesting the interchangeability of ROI volume for correlational studies.

Hippocampal shape across the healthy lifespan and its relationship with cognition

The study of the hippocampus across the healthy adult lifespan has rendered inconsistent findings. While volumetric measurements have often been a popular technique for analysis, more advanced morphometric techniques have demonstrated compelling results that highlight the importance and improved specificity of shape-based measures. Here, the MAGeT Brain algorithm was applied on 134 healthy individuals aged 18-81 years old to extract hippocampal subfield volumes and hippocampal shape measurements, namely: local surface area (SA) and displacement. We used linear-, second- or third-order natural splines to examine the relationships between hippocampal measures and age. In addition, partial least squares analyses were performed to relate volume and shape measurements with cognitive and demographic information. Volumetric results indicated a relative preservation of the right cornus ammonis 1 with age and a global volume reduction linked with older age, female sex, lower levels of education and cognitive performance. Vertex-wise analysis demonstrated an SA preservation in the anterior hippocampus with a peak during the sixth decade, while the posterior hippocampal SA gradually decreased across lifespan. Overall, SA decrease was linked to older age, female sex and, to a lesser extent lower levels of education and cognitive performance. Outward displacement in the lateral hippocampus and inward displacement in the medial hippocampus were enlarged with older age, lower levels of cognition and education, indicating an accentuation of the hippocampal "C" shape with age. Taken together, our findings suggest that vertex-wise analyses have higher spatial specifity and that sex, education, and cognition are implicated in the differential impact of age on hippocampal subregions throughout its anteroposterior and medial-lateral axes. This article is part of the Virtual Special Issue titled COGNITIVE NEU- ROSCIENCE OF HEALTHY AND PATHOLOGICAL AGING. The full issue can be found on ScienceDirect at https://www.sciencedirect.com/journal/neurobiology-of-aging/special-issue/105379XPWJP.

Dump the "dimorphism": Comprehensive synthesis of human brain studies reveals few male-female differences beyond size

With the explosion of neuroimaging, differences between male and female brains have been exhaustively analyzed. Here we synthesize three decades of human MRI and postmortem data, emphasizing meta-analyses and other large studies, which collectively reveal few reliable sex/gender differences and a history of unreplicated claims. Males' brains are larger than females' from birth, stabilizing around 11 % in adults. This size difference accounts for other reproducible findings: higher white/gray matter ratio, intra- versus interhemispheric connectivity, and regional cortical and subcortical volumes in males. But when structural and lateralization differences are present independent of size, sex/gender explains only about 1% of total variance. Connectome differences and multivariate sex/gender prediction are largely based on brain size, and perform poorly across diverse populations. Task-based fMRI has especially failed to find reproducible activation differences between men and women in verbal, spatial or emotion processing due to high rates of false discovery. Overall, male/female brain differences appear trivial and population-specific. The human brain is not "sexually dimorphic."

Cervical Dystonia Is Associated With Aberrant Inhibitory Signaling Within the Thalamus

Objective: The objective of this study is to investigate whether alterations in the neurotransmission of gamma-aminobutyric acid (GABA) in the thalamus are present in patients with cervical dystonia compared to healthy controls.

Methods: GABA magnetic resonance spectroscopy was used to investigate concentration levels of GABA in the thalamus of cervical dystonia patients (n = 17) compared to healthy controls (n = 18). Additionally, a focused post hoc analysis of thalamic GABA_A receptor availability data in a similar cohort (n = 15 for both groups) using data from a previously collected ¹¹C-flumazenil positron emission tomography study was performed. Group comparisons for all evaluations were performed using two-sided t-tests with adjustments for age and sex, and Bonferroni correction for multiple comparisons was applied. Spearman's coefficient was used to test correlations.

Results: We found significantly reduced GABA+/Cre levels in the thalamus of cervical dystonia patients compared to controls, and these levels positively correlated with disease duration. Although mean thalamic GABA_A receptor availability did not differ between patients and controls, GABA_A availability negatively correlated with both disease duration and dystonia severity.

Conclusions: These findings support that aberrant inhibitory signaling within the thalamus contributes to the pathophysiology of cervical dystonia. Additionally, these results suggest that an inadequate ability to compensate for the loss of GABA through upregulation of GABA_A receptors may underlie more severe symptoms.

Multi-contrast anatomical subcortical structures parcellation

The human subcortex is comprised of more than 450 individual nuclei which lie deep in the brain. Due to their small size and close proximity, up until now only 7% have been depicted in standard MRI atlases. Thus, the human subcortex can largely be considered as terra incognita. Here, we present a new open-source parcellation algorithm to automatically map the subcortex. The new algorithm has been tested on 17 prominent subcortical structures based on a large quantitative MRI dataset at 7 Tesla. It has been carefully validated against expert human raters and previous methods, and can easily be extended to other subcortical structures and applied to any quantitative MRI dataset. In sum, we hope this novel parcellation algorithm will facilitate functional and structural neuroimaging research into small subcortical nuclei and help to chart terra incognita.

Apathy is not associated with reduced ventral striatal volume in patients with schizophrenia

BACKGROUND

A growing body of neuroimaging research has revealed a relationship between blunted activation of the ventral striatum (VS) and apathy in schizophrenia. In contrast, the association between reduced striatal volume and apathy is less well established, while the relationship between VS function and structure in patients with schizophrenia remains an open question. Here, we aimed to replicate previous structural findings in a larger independent sample and to investigate the relationship between VS hypoactivation and VS volume.

METHODS

We included brain structural magnetic resonance imaging (MRI) data from 60 patients with schizophrenia (SZ) that had shown an association of VS hypoactivation with apathy during reward anticipation and 58 healthy controls (HC). To improve replicability, we applied analytical methods employed in two previously published studies: Voxel-based morphometry and the Multiple Automatically Generated Templates (MAGeT) algorithm. VS and dorsal striatum (DS) volume were correlated with apathy correcting for age, gender and total brain volume. Additionally, left VS activity was correlated with left VS volume.

RESULTS

We failed to replicate the association between apathy and reduced VS volume and did not find a correlation with DS volume. Functional and structural left VS measures exhibited a trend-level correlation (r_s = 0.248, p = 0.067, r² = 0.06).

CONCLUSIONS

Our present data suggests that functional and structural striatal neuroimaging correlates of apathy can occur independently. Replication of previous findings may have been limited by other factors (medication, illness duration, age) potentially related to striatal volume changes in SZ. Finally, associations between reward-related VS function and structure should be further explored.

Do Unremitted Psychotic Symptoms Have an Effect on the Brain? A 2-Year Follow-up Imaging Study in First-Episode Psychosis

Background

To examine whether the duration of unremitted psychotic symptoms after the onset of a first episode of psychosis (FEP) is associated with cortical thickness and hippocampal volume, as well as structural covariance of these measures.

Method

Longitudinal MRI scans were obtained for 80 FEP patients shortly after entry to FEP clinic (baseline), and then 12 months and 24 months later. The proportion of time patients experienced unremitted positive symptoms for 2 interscan intervals (baseline to 12 mo, 12 mo to 24 mo) was calculated. Changes in cortical thickness and hippocampal volumes were calculated for each interscan interval and associated with duration of unremitted psychotic symptoms. Significant regions were then used in seed-based structural covariance analyses to examine the effect of unremitted psychotic symptoms on brain structural organization. Importantly, analyses controlled for antipsychotic medication.

Results

Cortical thinning within the left medial/orbitofrontal prefrontal cortex and superior temporal gyrus were significantly associated with the duration of unremitted psychotic symptoms during the first interscan interval (ie, baseline to 12 mo). Further, changes in cortical thickness within the left medial/orbitofrontal cortex positively covaried with changes in thickness in the left dorsal and ventrolateral prefrontal cortex during this period. No associations were observed during the second interscan interval, nor with hippocampal volumes.

Conclusions

These results demonstrate that cortical thickness change can be observed shortly after an FEP, and these changes are proportionally related to the percentage of time spent with unremitted psychotic symptoms. Altered structural covariance in the prefrontal cortex suggests that unremitted psychotic symptoms may underlie reorganization in higher-order cortical regions.

Material hardship, prefrontal cortex-amygdala structure, and internalizing symptoms in children

Material hardship, or difficulty affording basic resources such as food, housing, utilities, and health care, increases children's risk for internalizing problems. The uncinate fasciculus (UNC) and two of the gray matter regions it connects-the orbitofrontal cortex (OFC) and amygdala-may play important roles in the neural mechanisms underlying these associations. We investigated associations among material hardship, UNC microstructure, OFC and amygdala structure, and internalizing symptoms in children. Participants were 5-9-year-old children (N = 94, 61% female) from socioeconomically diverse families. Parents completed questionnaires assessing material hardship and children's internalizing symptoms. High-resolution, T1-weighted magnetic resonance imaging (n = 51), and diffusion tensor imaging (n = 58) data were acquired. UNC fractional anisotropy (FA), medial OFC surface area, and amygdala gray matter volume were extracted. Greater material hardship was significantly associated with lower UNC FA, smaller amygdala volume, and higher internalizing symptoms in children, after controlling for age, sex, and family income-to-needs ratio. Lower UNC FA significantly mediated the association between material hardship and internalizing symptoms in girls but not boys. These findings are consistent with the notion that material hardship may lead to altered white matter microstructure and gray matter structure in neural networks critical to emotion processing and regulation.

Characterizing the Subcortical Structures in Youth with Congenital Heart Disease

BACKGROUND AND PURPOSE

Congenital heart disease is a leading cause of neurocognitive impairment. Many subcortical structures are known to play a crucial role in higher-order cognitive processing. However, comprehensive anatomic characterization of these structures is currently lacking in the congenital heart disease population. Therefore, this study aimed to compare the morphometry and volume of the globus pallidus, striatum, and thalamus between youth born with congenital heart disease and healthy peers.

MATERIALS AND METHODS

We recruited youth between 16 and 24 years of age born with congenital heart disease who underwent cardiopulmonary bypass surgery before 2 years of age (n = 48) and healthy controls of the same age (n = 48). All participants underwent a brain MR imaging to acquire high-resolution 3D T1-weighted images.

RESULTS

Smaller surface area and inward bilateral displacement across the lateral surfaces of the globus pallidus were concentrated anteriorly in the congenital heart disease group compared with controls (q < 0.15). On the lateral surfaces of bilateral thalami, we found regions of both larger and smaller surface areas, as well as inward and outward displacement in the congenital heart disease group compared with controls (q < 0.15). We did not find any morphometric differences between groups for the striatum. For the volumetric analyses, only the right globus pallidus showed a significant volume reduction (q < 0.05) in the congenital heart disease group compared with controls.

CONCLUSIONS

This study reports morphometric alterations in youth with congenital heart disease in the absence of volume reductions, suggesting that volume alone is not sufficient to detect and explain subtle neuroanatomic differences in this clinical population.

Morphometric in vivo evidence of thalamic atrophy correlated with cognitive and motor dysfunction in Huntington's disease: The IMAGE-HD study

In Huntington's disease (HD), neurodegeneration causes progressive atrophy to the striatum, cortical areas, and white matter tracts - components of corticostriatal circuitry. Such processes may affect the thalamus, a key circuit node. We investigated whether differences in dorsal thalamic morphology were detectable in HD, and whether thalamic atrophy was associated with neurocognitive, neuropsychiatric and motor dysfunction. Magnetic resonance imaging scans and clinical outcome measures were obtained from 34 presymptomatic HD (pre-HD), 29 early symptomatic HD (symp-HD), and 26 healthy control individuals who participated in the IMAGE-HD study. Manual region of interest (ROI) segmentation was conducted to measure dorsal thalamic volume, and thalamic ROI underwent shape analysis using the spherical harmonic point distribution method. The symp-HD group had significant thalamic volumetric reduction and global shape deflation, indicative of atrophy, compared to pre-HD and control groups. Thalamic atrophy significantly predicted neurocognitive and motor dysfunction within the symp-HD group only. Thalamic morphology differentiates symp-HD from pre-HD and healthy individuals. Thalamic changes may be one of the structural bases (endomorphotypes), of the endophenotypic neurocognitive and motor manifestations of disease. Future research should continue to investigate the thalamus as a potential in vivo biomarker of disease progression in HD.

Investigating the Added Value of FreeSurfer's Manual Editing Procedure for the Study of the Reading Network in a Pediatric Population

Insights into brain anatomy are important for the early detection of neurodevelopmental disorders, such as dyslexia. FreeSurfer is one of the most frequently applied automatized software tools to study brain morphology. However, quality control of the outcomes provided by FreeSurfer is often ignored and could lead to wrong statistical inferences. Additional manual editing of the data may be a solution, although not without a cost in time and resources. Past research in adults on comparing the automatized method of FreeSurfer with and without additional manual editing indicated that although editing may lead to significant differences in morphological measures between the methods in some regions, it does not substantially change the sensitivity to detect clinical differences. Given that automated approaches are more likely to fail in pediatric-and inherently more noisy-data, we investigated in the current study whether FreeSurfer can be applied fully automatically or additional manual edits of T1-images are needed in a pediatric sample. Specifically, cortical thickness and surface area measures with and without additional manual edits were compared in six regions of interest (ROIs) of the reading network in 5-to-6-year-old children with and without dyslexia. Results revealed that additional editing leads to statistical differences in the morphological measures, but that these differences are consistent across subjects and that the sensitivity to reveal statistical differences in the morphological measures between children with and without dyslexia is not affected, even though conclusions of marginally significant findings can differ depending on the method used. Thereby, our results indicate that additional manual editing of reading-related regions in FreeSurfer has limited gain for pediatric samples.

Subcortical shape alterations in major depressive disorder: Findings from the ENIGMA major depressive disorder working group

Alterations in regional subcortical brain volumes have been investigated as part of the efforts of an international consortium, ENIGMA, to identify reliable neural correlates of major depressive disorder (MDD). Given that subcortical structures are comprised of distinct subfields, we sought to build significantly from prior work by precisely mapping localized MDD‐related differences in subcortical regions using shape analysis. In this meta‐analysis of subcortical shape from the ENIGMA‐MDD working group, we compared 1,781 patients with MDD and 2,953 healthy controls (CTL) on individual measures of shape metrics (thickness and surface area) on the surface of seven bilateral subcortical structures: nucleus accumbens, amygdala, caudate, hippocampus, pallidum, putamen, and thalamus. Harmonized data processing and statistical analyses were conducted locally at each site, and findings were aggregated by meta‐analysis. Relative to CTL, patients with adolescent‐onset MDD (≤ 21 years) had lower thickness and surface area of the subiculum, cornu ammonis (CA) 1 of the hippocampus and basolateral amygdala (Cohen's d = −0.164 to −0.180). Relative to first‐episode MDD, recurrent MDD patients had lower thickness and surface area in the CA1 of the hippocampus and the basolateral amygdala (Cohen's d = −0.173 to −0.184). Our results suggest that previously reported MDD‐associated volumetric differences may be localized to specific subfields of these structures that have been shown to be sensitive to the effects of stress, with important implications for mapping treatments to patients based on specific neural targets and key clinical features.

On brain atlas choice and automatic segmentation methods: a comparison of MAPER & FreeSurfer using three atlas databases

Several automatic image segmentation methods and few atlas databases exist for analysing structural T1-weighted magnetic resonance brain images. The impact of choosing a combination has not hitherto been described but may bias comparisons across studies. We evaluated two segmentation methods (MAPER and FreeSurfer), using three publicly available atlas databases (Hammers_mith, Desikan-Killiany-Tourville, and MICCAI 2012 Grand Challenge). For each combination of atlas and method, we conducted a leave-one-out cross-comparison to estimate the segmentation accuracy of FreeSurfer and MAPER. We also used each possible combination to segment two datasets of patients with known structural abnormalities (Alzheimer's disease (AD) and mesial temporal lobe epilepsy with hippocampal sclerosis (HS)) and their matched healthy controls. MAPER was better than FreeSurfer at modelling manual segmentations in the healthy control leave-one-out analyses in two of the three atlas databases, and the Hammers_mith atlas database transferred to new datasets best regardless of segmentation method. Both segmentation methods reliably identified known abnormalities in each patient group. Better separation was seen for FreeSurfer in the AD and left-HS datasets, and for MAPER in the right-HS dataset. We provide detailed quantitative comparisons for multiple anatomical regions, thus enabling researchers to make evidence-based decisions on their choice of atlas and segmentation method.

Fully Automated Habenula Segmentation Provides Robust and Reliable Volume Estimation Across Large Magnetic Resonance Imaging Datasets, Suggesting Intriguing Developmental Trajectories in Psychiatric Disease

Studies of habenula (Hb) function and structure provided evidence of its involvement in psychiatric disorders, including schizophrenia and bipolar disorder. Previous studies using magnetic resonance imaging (manual/semiautomated segmentation) have reported conflicting results. Aiming to improve Hb segmentation reliability and the study of large datasets, we describe a fully automated protocol that was validated against manual segmentations and applied to 3 datasets (childhood/adolescence and adult bipolar disorder and schizophrenia). It achieved reliable Hb segmentation, providing robust volume estimations across a large age range and varying image acquisition parameters. Applying it to clinically relevant datasets, we found smaller Hb volumes in the adult bipolar disorder dataset and larger volumes in the adult schizophrenia dataset compared with healthy control subjects. There are indications that Hb volume in both groups shows deviating developmental trajectories early in life. This technique sets a precedent for future studies, as it allows for fast and reliable Hb segmentation and will be publicly available.

Imputation Strategy for Reliable Regional MRI Morphological Measurements

Regional morphological analysis represents a crucial step in most neuroimaging studies. Results from brain segmentation techniques are intrinsically prone to certain degrees of variability, mainly as results of suboptimal segmentation. To reduce this inherent variability, the errors are often identified through visual inspection and then corrected (semi)manually. Identification and correction of incorrect segmentation could be very expensive for large-scale studies. While identification of the incorrect results can be done relatively fast even with manual inspection, the correction step is extremely time-consuming, as it requires training staff to perform laborious manual corrections. Here we frame the correction phase of this problem as a missing data problem. Instead of manually adjusting the segmentation outputs, our computational approach aims to derive accurate morphological measures by machine learning imputation. Data imputation techniques may be used to replace missing or incorrect region average values with carefully chosen imputed values, all of which are computed based on other available multivariate information. We examined our approach of correcting segmentation outputs on a cohort of 970 subjects, which were undergone an extensive, time-consuming, manual post-segmentation correction. A random forest imputation technique recovered the gold standard results with a significant accuracy (r = 0.93, p < 0.0001; when 30% of the segmentations were considered incorrect in a non-random fashion). The random forest technique proved to be most effective for big data studies (N > 250).

Interactive effects of age and recent substance use on striatal shape morphology at substance use disorder treatment entry

BACKGROUND

Striatal neuroadaptations are regarded to play an important role in the progression from voluntary to compulsive use of addictive substances and provide a promising target for the identification of neuroimaging biomarkers. Recent advances in surface-based computational analysis enable morphological assessment linking variations in global and local striatal shape to duration and magnitude of substance use with a degree of sensitivity that exceeds standard volumetric analysis.

METHODS

This study used a new segmentation methodology coupled with local surface-based indices of surface area and displacement to provide a comprehensive structural characterization of the striatum in 34 patients entering treatment for substance use disorder (SUD) and 49 controls, and to examine the influence of recent substance use on abnormal age-related striatal deformation in SUD patients.

RESULTS

Patients showed a small reduction in striatal volume and no difference in surface area or shape in comparison to controls. Between-group differences in shape were likely neutralized by the bidirectional influence of recent substance use on striatal shape in SUD patients. Specifically, there was an interaction between age and substance such that among older patients more drug use was associated with greater inward striatal contraction but more alcohol use was associated with greater outward expansion.

CONCLUSIONS

This study builds on previous work and advances our understanding of the nature of striatal neuroadaptations as a potential biomarker of disease progression in addiction.

MR-based age-related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan

While numerous studies have used magnetic resonance imaging (MRI) to elucidate normative age-related trajectories in subcortical structures across the human lifespan, there exists substantial heterogeneity among different studies. Here, we investigated the normative relationships between age and morphology (i.e., volume and shape), and microstructure (using the T1-weighted/T2-weighted [T1w/T2w] signal ratio as a putative index of myelin and microstructure) of the striatum, globus pallidus, and thalamus across the adult lifespan using a dataset carefully quality controlled, yielding a final sample of 178 for the morphological analyses, and 162 for the T1w/T2w analyses from an initial dataset of 253 healthy subjects, aged 18-83. In accordance with previous cross-sectional studies of adults, we observed age-related volume decrease that followed a quadratic relationship between age and bilateral striatal and thalamic volumes, and a linear relationship in the globus pallidus. Our shape indices consistently demonstrated age-related posterior and medial areal contraction bilaterally across all three structures. Beyond morphology, we observed a quadratic inverted U-shaped relationship between T1w/T2w signal ratio and age, with a peak value occurring in middle age (at around 50 years old). After permutation testing, the Akaike information criterion determined age relationships remained significant for the bilateral globus pallidus and thalamus, for both the volumetric and T1w/T2w analyses. Our findings serve to strengthen and expand upon previous volumetric analyses by providing a normative baseline of morphology and microstructure of these structures to which future studies investigating patients with various disorders can be compared.

The effect of second-generation antipsychotics on basal ganglia and thalamus in first-episode psychosis patients

Patients who have recently experienced a first of episode psychosis (FEP) exhibit considerable heterogeneity in subcortical brain volumes. These results become even more divergent when exploring the effect of antipsychotic medication among other clinical and cognitive features. We aimed to contrast volumetric measures in basal ganglia and thalamus in patients with a FEP treated with different second-generation antipsychotics. T1-weighted magnetic resonance images were obtained and subcortical structures were extracted with MAGeT-Brain. Relationships with cognitive functioning were also explored with a Global Cognitive Index obtained, on average, within one month from the scan. Subgroups included: risperidone (n = 26), aripiprazole (n = 22), olanzapine (n = 19) and controls (n = 80). The olanzapine subgroup displayed significant enlargement of the right globus pallidus volume compared with all other groups. Moreover, despite not exhibiting poorer cognitive capacity than the rest of patients, results from a stepwise multiple-regression linear regression analysis identified a significant negative association between right globus pallidus volume and scores on the Global Cognitive Index among these patients. To our knowledge, this is the first study to associate treatment with olanzapine with an increase in globus pallidus volume in a sample of FEP patients with a relatively short time of antipsychotic monotherapy. Such enlargement was also found to be associated with poorer global cognitive functioning. Exploration of the biological underpinnings of this early medication-induced enlargement should be the focus of future investigations since it may lend insight towards achieving a better clinical outcome for these patients.

Image processing and analysis methods for the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data is a resource of unprecedented scale and depth for studying typical and atypical development. The aim of this manuscript is to describe the baseline neuroimaging processing and subject-level analysis methods used by ABCD. Processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI. This manuscript serves as a methodological reference for users of publicly shared neuroimaging data from the ABCD Study.

Parental punitive discipline and children's depressive symptoms: Associations with striatal volume

Although parental harshness has been consistently linked with increased depressive symptoms in youth, its associations with children's brain structure are not well understood. The striatum has been strongly implicated in depression in adolescents and adults. In this study, we investigated the associations among parental harsh discipline, striatal volume, and depressive symptoms in children. Participants were parents and their 5- to 9-year-old children (63% female; 29% African American; 47% Hispanic/Latino). Parents completed questionnaires about their parenting behaviors and children's depressive symptoms. Children participated in a high-resolution, T1-weighted MRI scan, and volumetric data for the caudate, putamen, and nucleus accumbens were extracted (n = 20 with both parenting and MRI data; n = 48 with both MRI and depressive symptom data). Findings indicated that more frequent parental harsh discipline was significantly associated with smaller dorsal striatal volume (caudate plus putamen). In addition, smaller dorsal striatal volume was significantly associated with increased depressive symptoms in children. These associations remained significant after accounting for child age, sex, whole brain volume, and parental depressive symptoms. These findings suggest that parental harsh discipline may be associated with children's striatal volume, which may in turn be associated with their level of depressive symptoms.

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.

Effect of disease-modifying therapies on subcortical gray matter atrophy in multiple sclerosis

BACKGROUND

The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear.

OBJECTIVE

To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS.

METHODS

A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category.

RESULTS

A total of 86 DMT epochs (DMT-H: n = 32; DMT-L: n = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (-0.15% vs -0.81%; p = 0.001) and putaminal (-0.27% vs -0.73%; p = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume.

CONCLUSION

DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS.

Clarifying associations between cortical thickness, subcortical structures, and a comprehensive assessment of clinical insight in enduring schizophrenia

BACKGROUND

The relationship between poor insight and less favorable outcomes in schizophrenia has promoted research efforts to understand its neurobiological basis. Thus far, research on neural correlates of insight has been constrained by small samples, incomplete insight assessments, and a focus on frontal lobes. The purpose of this study was to examine associations of cortical thickness and subcortical volumes, with a comprehensive assessment of clinical insight, in a large sample of enduring schizophrenia patients.

METHODS

Two dimensions of clinical insight previously identified by a factor analysis of 4 insight assessments were used: Awareness of Illness and Need for Treatment (AINT) and Awareness of Symptoms and Consequences (ASC). T1-weighted structural images were acquired on a 3 T MRI scanner for 110 schizophrenia patients and 69 healthy controls. MR images were processed using CIVET (version 2.0) and MAGeT and quality controlled pre and post-processing. Whole-brain and region-of-interest, vertex-wise linear models were applied between cortical thickness, and levels of AINT and ASC. Partial correlations were conducted between volumes of the amygdala, thalamus, striatum, and hippocampus and insight levels.

RESULTS

No significant associations between both insight factors and cortical thickness were observed. Moreover, no significant associations emerged between subcortical volumes and both insight factors.

CONCLUSIONS

These results do not replicate previous findings obtained with smaller samples using single-item measures of insight into illness, suggesting a limited role of neurobiological factors and a greater role of psychological processes in explaining levels of clinical insight.

Longitudinal changes in cocaine intake and cognition are linked to cortical thickness adaptations in cocaine users

BACKGROUND

Cocaine use has been consistently associated with decreased gray matter volumes in the prefrontal cortex. However, it is unclear if such neuroanatomical abnormalities depict either pre-existing vulnerability markers or drug-induced consequences. Thus, this longitudinal MRI study investigated neuroplasticity and cognitive changes in relation to altered cocaine intake.

METHODS

Surface-based morphometry, cocaine hair concentration, and cognitive performance were measured in 29 cocaine users (CU) and 38 matched controls at baseline and follow-up. Based on changes in hair cocaine concentration, CU were classified either as Decreasers (n = 15) or Sustained Users (n = 14). Surface-based morphometry measures did not include regional tissue volumes.

RESULTS

At baseline, CU displayed reduced cortical thickness (CT) in lateral frontal regions, and smaller cortical surface area (CSA) in the anterior cingulate cortex, compared to controls. In Decreasers, CT of the lateral frontal cortex increased whereas CT within the same regions tended to further decrease in Sustained Users. In contrast, no changes were found for CSA and subcortical structures. Changes in CT were linked to cognitive performance changes and amount of cocaine consumed over the study period.

CONCLUSIONS

These results suggest that frontal abnormalities in CU are partially drug-induced and can recover with decreased substance use. Moreover, recovery of frontal CT is accompanied by improved cognitive performance confirming that cognitive decline associated with cocaine use is potentially reversible.

Trait impulsivity is not related to post-commissural putamen volumes: A replication study in healthy men

High levels of trait impulsivity are considered a risk factor for substance abuse and drug addiction. We recently found that non-planning trait impulsivity was negatively correlated with post-commissural putamen volumes in men, but not women, using the Karolinska Scales of Personality (KSP). Here, we attempted to replicate this finding in an independent sample using an updated version of the KSP: the Swedish Universities Scales of Personality (SSP). Data from 88 healthy male participants (Mean Age: 28.16±3.34), who provided structural T1-weighted magnetic resonance images (MRIs) and self-reported SSP impulsivity scores, were analyzed. Striatal sub-region volumes were acquired using the Multiple Automatically Generated Templates (MAGeT-Brain) algorithm. Contrary to our previous findings trait impulsivity measured using SSP was not a significant predictor of post-commissural putamen volumes (β = .14, df = 84, p = .94). A replication Bayes Factors analysis strongly supported this null result. Consistent with our previous findings, secondary exploratory analyses found no relationship between ventral striatum volumes and SSP trait impulsivity (β = -.05, df = 84, p = .28). An exploratory analysis of the other striatal compartments showed that there were no significant associations with trait impulsivity. While we could not replicate our previous findings in the current sample, we believe this work will aide future studies aimed at establishing meaningful brain biomarkers for addiction vulnerability in healthy humans.

An intrinsic association between olfactory identification and spatial memory in humans

It was recently proposed that olfaction evolved to aid navigation. Consistent with this hypothesis, olfactory identification and spatial memory are linked to overlapping brain areas which include the orbitofrontal cortex and hippocampus. However, the relationship between these two processes has never been specifically investigated. Here, we show that olfactory identification covaries with spatial memory in humans. We also found that the cortical thickness of the left medial orbitofrontal cortex, and the volume of the right hippocampus, predict both olfactory identification and spatial memory. Finally, we demonstrate deficits in both olfactory identification and spatial memory in patients with lesions of the medial orbitofrontal cortex. Our findings reveal an intrinsic relationship between olfaction and spatial memory that is supported by a shared reliance on the hippocampus and medial orbitofrontal cortex. This relationship may find its roots in the parallel evolution of the olfactory and hippocampal systems.

Olfaction, the sense of smell, may have originally evolved to aid navigation in space, but there is no direct evidence of a link between olfaction and navigation in humans. Here the authors show that olfaction and spatial memory abilities are correlated and rely on similar brain regions in humans.

Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei

Nonlinear registration of individual brain MRI scans to standard brain templates is common practice in neuroimaging and multiple registration algorithms have been developed and refined over the last 20 years. However, little has been done to quantitatively compare the available algorithms and much of that work has exclusively focused on cortical structures given their importance in the fMRI literature. In contrast, for clinical applications such as functional neurosurgery and deep brain stimulation (DBS), proper alignment of subcortical structures between template and individual space is important. This allows for atlas-based segmentations of anatomical DBS targets such as the subthalamic nucleus (STN) and internal pallidum (GPi). Here, we systematically evaluated the performance of six modern and established algorithms on subcortical normalization and segmentation results by calculating over 11,000 nonlinear warps in over 100 subjects. For each algorithm, we evaluated its performance using T1-or T2-weighted acquisitions alone or a combination of T1-, T2-and PD-weighted acquisitions in parallel. Furthermore, we present optimized parameters for the best performing algorithms. We tested each algorithm on two datasets, a state-of-the-art MRI cohort of young subjects and a cohort of subjects age- and MR-quality-matched to a typical DBS Parkinson's Disease cohort. Our final pipeline is able to segment DBS targets with precision comparable to manual expert segmentations in both cohorts. Although the present study focuses on the two prominent DBS targets, STN and GPi, these methods may extend to other small subcortical structures like thalamic nuclei or the nucleus accumbens.

Carriage of Supernumerary Sex Chromosomes Decreases the Volume and Alters the Shape of Limbic Structures

Sex chromosome aneuploidy (SCA) increases risk for several psychiatric disorders associated with the limbic system, including mood and autism spectrum disorders. Thus, SCA offers a genetics-first model for understanding the biological basis of psychopathology. Additionally, the sex-biased prevalence of many psychiatric disorders could potentially reflect sex chromosome dosage effects on brain development. To clarify how limbic anatomy varies across sex and sex chromosome complement, we characterized amygdala and hippocampus structure in a uniquely large sample of patients carrying supernumerary sex chromosomes (n = 132) and typically developing controls (n = 166). After adjustment for sex-differences in brain size, karyotypically normal males (XY) and females (XX) did not differ in volume or shape of either structure. In contrast, all SCAs were associated with lowered amygdala volume relative to gonadally-matched controls. This effect was robust to three different methods for total brain volume adjustment, including an allometric analysis that derived normative scaling rules for these structures in a separate, typically developing population (n = 79). Hippocampal volume was insensitive to SCA after adjustment for total brain volume. However, surface-based analysis revealed that SCA, regardless of specific karyotype, was consistently associated with a spatially specific pattern of shape change in both amygdala and hippocampus. In particular, SCA was accompanied by contraction around the basomedial nucleus of the amygdala and an area crossing the hippocampal tail. These results demonstrate the power of SCA as a model to understand how copy number variation can precipitate changes in brain systems relevant to psychiatric disease.

Warping an atlas derived from serial histology to 5 high-resolution MRIs

Previous work from our group demonstrated the use of multiple input atlases to a modified multi-atlas framework (MAGeT-Brain) to improve subject-based segmentation accuracy. Currently, segmentation of the striatum, globus pallidus and thalamus are generated from a single high-resolution and -contrast MRI atlas derived from annotated serial histological sections. Here, we warp this atlas to five high-resolution MRI templates to create five de novo atlases. The overall goal of this work is to use these newly warped atlases as input to MAGeT-Brain in an effort to consolidate and improve the workflow presented in previous manuscripts from our group, allowing for simultaneous multi-structure segmentation. The work presented details the methodology used for the creation of the atlases using a technique previously proposed, where atlas labels are modified to mimic the intensity and contrast profile of MRI to facilitate atlas-to-template nonlinear transformation estimation. Dice's Kappa metric was used to demonstrate high quality registration and segmentation accuracy of the atlases. The final atlases are available at https://github.com/CobraLab/atlases/tree/master/5-atlas-subcortical.

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.

Multiple sclerosis–related fatigue: Altered resting-state functional connectivity of the ventral striatum and dorsolateral prefrontal cortex

Objective:

Since recent studies suggested a role of the striatum and prefrontal cortex for multiple sclerosis (MS)-related fatigue, we investigated resting-state functional connectivity alterations of striatal subdivisions and the dorsolateral prefrontal cortex (dlPFC).

Methods:

Resting-state functional magnetic resonance imaging was acquired in 77 relapsing–remitting MS patients (38 fatigued (F-MS), 39 non-fatigued (NF-MS)) and 41 matched healthy controls (HC). Fatigue severity was assessed using the fatigue severity scale. Seed-based connectivity analyses were performed using subregions of the striatum and the dlPFC as regions of interest applying non-parametric permutation testing.

Results:

Compared to HC and NF-MS patients, F-MS patients showed reduced caudate nucleus and ventral striatum functional connectivity with the sensorimotor cortex (SMC) and frontal, parietal, and temporal cortex regions. Fatigue severity correlated negatively with functional connectivity of the caudate nucleus and ventral striatum with the SMC and positively with functional connectivity of the dlPFC with the rostral inferior parietal gyrus and SMC.

Conclusion:

MS-related fatigue is associated with reduced functional connectivity between the striatum and sensorimotor as well as attention and reward networks, in which the ventral striatum might be a key integration hub. Together with increased connectivity between the dlPFC and sensory cortical areas, these connectivity alterations shed light on the mechanisms of MS-related fatigue.