A learning-based wrapper method to correct systematic errors in automatic image segmentation: consistently improved performance in hippocampus, cortex and brain segmentation

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.

Connectivity of the Piriform Cortex and its Implications in Temporal Lobe Epilepsy

Background

The piriform cortex has been implicated in the initiation, spread and termination of epileptic seizures. This understanding has extended to surgical management of epilepsy, where it has been shown that resection or ablation of the piriform cortex can result in better outcomes. How and why the piriform cortex may play such a crucial role in seizure networks is not well understood. To answer these questions, we investigated the functional and structural connectivity of the piriform cortex in both healthy controls and temporal lobe epilepsy (TLE) patients.

Methods

We studied a retrospective cohort of 55 drug-resistant unilateral TLE patients and 26 healthy controls who received structural and functional neuroimaging. Using seed-to-voxel connectivity we compared the normative whole-brain connectivity of the piriform to that of the hippocampus, a region commonly involved in epilepsy, to understand the differential contribution of the piriform to the epileptogenic network. We subsequently measured the inter-piriform coupling (IPC) to quantify similarities in the inter-hemispheric cortical functional connectivity profile between the two piriform cortices. We related differences in IPC in TLE back to aberrations in normative piriform connectivity, whole brain functional properties, and structural connectivity.

Results

We find that relative to the hippocampus, the piriform is functionally connected to the anterior insula and the rest of the salience ventral attention network (SAN). We also find that low IPC is a sensitive metric of poor surgical outcome (sensitivity: 85.71%, 95% CI: [19.12%, 99.64%]); and differences in IPC within TLE were related to disconnectivity and hyperconnectivity to the anterior insula and the SAN. More globally, we find that low IPC is associated with whole-brain functional and structural segregation, marked by decreased functional small-worldness and fractional anisotropy.

Conclusions

Our study presents novel insights into the functional and structural neural network alterations associated with this structure, laying the foundation for future work to carefully consider its connectivity during the presurgical management of epilepsy.

Automated protocols for delineating human hippocampal subfields from 3 Tesla and 7 Tesla magnetic resonance imaging data

Researchers who study the human hippocampus are naturally interested in how its subfields function. However, many researchers are precluded from examining subfields because their manual delineation from magnetic resonance imaging (MRI) scans (still the gold standard approach) is time consuming and requires significant expertise. To help ameliorate this issue, we present here two protocols, one for 3T MRI and the other for 7T MRI, that permit automated hippocampus segmentation into six subregions, namely dentate gyrus/cornu ammonis (CA)4, CA2/3, CA1, subiculum, pre/parasubiculum, and uncus along the entire length of the hippocampus. These protocols are particularly notable relative to existing resources in that they were trained and tested using large numbers of healthy young adults (n = 140 at 3T, n = 40 at 7T) whose hippocampi were manually segmented by experts from MRI scans. Using inter-rater reliability analyses, we showed that the quality of automated segmentations produced by these protocols was high and comparable to expert manual segmenters. We provide full open access to the automated protocols, and anticipate they will save hippocampus researchers a significant amount of time. They could also help to catalyze subfield research, which is essential for gaining a full understanding of how the hippocampus functions.

The brain markers of creativity measured by divergent thinking in childhood: Hippocampal volume and functional connectivity

Creativity, a high-order cognitive ability, has received wide attention from researchers and educators who are dedicated to promoting its development throughout one's lifespan. Currently, creativity is commonly assessed with divergent thinking tasks, such as the Alternative Uses Task. Recent advancements in neuroimaging techniques have enabled the identification of brain markers for high-order cognitive abilities. One such brain structure of interest in this regard is the hippocampus, which has been found to play an important role in generating creative thoughts in adulthood. However, such role of the hippocampus in childhood is not clear. Thus, this study aimed to investigate the associations between creativity, as measured by divergent thinking, and both the volume of the hippocampus and its resting-state functional connectivity in 116 children aged 8-12 years. The results indicate significant relations between divergent thinking and the volume of the hippocampal head and the hippocampal tail, as well as the volume of a subfield comprising cornu ammonis 2-4 and dentate gyrus within the hippocampal body. Additionally, divergent thinking was significantly related to the differences between the anterior and the posterior hippocampus in their functional connectivity to other brain regions during rest. These results suggest that these two subregions may collaborate with different brain regions to support diverse cognitive processes involved in the generation of creative thoughts. In summary, these findings indicate that divergent thinking is significantly related to the structural and functional characteristics of the hippocampus, offering potential insights into the brain markers for creativity during the developmental stage.

Fetal brain MRI atlases and datasets: A review

Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.

Tau PET burden in Brodmann areas 35 and 36 is associated with individual differences in cognition in non-demented older adults

Introduction

The accumulation of neurofibrillary tau tangles, a neuropathological hallmark of Alzheimer's disease (AD), occurs in medial temporal lobe (MTL) regions early in the disease process, with some of the earliest deposits localized to subregions of the entorhinal cortex. Although functional specialization of entorhinal cortex subregions has been reported, few studies have considered functional associations with localized tau accumulation.

Methods

In this study, stepwise linear regressions were used to examine the contributions of regional tau burden in specific MTL subregions, as measured by 18F-MK6240 PET, to individual variability in cognition. Dependent measures of interest included the Clinical Dementia Rating Sum of Boxes (CDR-SB), Mini Mental State Examination (MMSE), and composite scores of delayed episodic memory and language. Other model variables included age, sex, education, APOE4 status, and global amyloid burden, indexed by 11C-PiB.

Results

Tau burden in right Brodmann area 35 (BA35), left and right Brodmann area 36 (BA36), and age each uniquely contributed to the proportion of explained variance in CDR-SB scores, while right BA36 and age were also significant predictors of MMSE scores, and right BA36 was significantly associated with delayed episodic memory performance. Tau burden in both left and right BA36, along with education, uniquely contributed to the proportion of explained variance in language composite scores. Importantly, the addition of more inclusive ROIs, encompassing less granular segmentation of the entorhinal cortex, did not significantly contribute to explained variance in cognition across any of the models.

Discussion

These findings suggest that the ability to quantify tau burden in more refined MTL subregions may better account for individual differences in cognition, which may improve the identification of non-demented older adults who are on a trajectory of decline due to AD.

Cocaine Use Prediction With Tensor-Based Machine Learning on Multimodal MRI Connectome Data

This letter considers the use of machine learning algorithms for predicting cocaine use based on magnetic resonance imaging (MRI) connectomic data. The study used functional MRI (fMRI) and diffusion MRI (dMRI) data collected from 275 individuals, which was then parcellated into 246 regions of interest (ROIs) using the Brainnetome atlas. After data preprocessing, the data sets were transformed into tensor form. We developed a tensor-based unsupervised machine learning algorithm to reduce the size of the data tensor from 275 (individuals) × 2 (fMRI and dMRI) × 246 (ROIs) × 246 (ROIs) to 275 (individuals) × 2 (fMRI and dMRI) × 6 (clusters) × 6 (clusters). This was achieved by applying the high-order Lloyd algorithm to group the ROI data into six clusters. Features were extracted from the reduced tensor and combined with demographic features (age, gender, race, and HIV status). The resulting data set was used to train a Catboost model using subsampling and nested cross-validation techniques, which achieved a prediction accuracy of 0.857 for identifying cocaine users. The model was also compared with other models, and the feature importance of the model was presented. Overall, this study highlights the potential for using tensor-based machine learning algorithms to predict cocaine use based on MRI connectomic data and presents a promising approach for identifying individuals at risk of substance abuse.

Automated and manual segmentation of the hippocampus in human infants

The hippocampus, critical for learning and memory, undergoes substantial changes early in life. Investigating the developmental trajectory of hippocampal structure and function requires an accurate method for segmenting this region from anatomical MRI scans. Although manual segmentation is regarded as the "gold standard" approach, it is laborious and subjective. This has fueled the pursuit of automated segmentation methods in adults. However, little is known about the reliability of these automated protocols in infants, particularly when anatomical scan quality is degraded by head motion or the use of shorter and quieter infant-friendly sequences. During a task-based fMRI protocol, we collected quiet T1-weighted anatomical scans from 42 sessions with awake infants aged 4-23 months. Two expert tracers first segmented the hippocampus in both hemispheres manually. The resulting inter-rater reliability (IRR) was only moderate, reflecting the difficulty of infant segmentation. We then used four protocols to predict these manual segmentations: average adult template, average infant template, FreeSurfer software, and Automated Segmentation of Hippocampal Subfields (ASHS) software. ASHS generated the most reliable hippocampal segmentations in infants, exceeding the manual IRR of experts. Automated methods thus provide robust hippocampal segmentations of noisy T1-weighted infant scans, opening new possibilities for interrogating early hippocampal development.

Deep label fusion: A generalizable hybrid multi-atlas and deep convolutional neural network for medical image segmentation

Deep convolutional neural networks (DCNN) achieve very high accuracy in segmenting various anatomical structures in medical images but often suffer from relatively poor generalizability. Multi-atlas segmentation (MAS), while less accurate than DCNN in many applications, tends to generalize well to unseen datasets with different characteristics from the training dataset. Several groups have attempted to integrate the power of DCNN to learn complex data representations and the robustness of MAS to changes in image characteristics. However, these studies primarily focused on replacing individual components of MAS with DCNN models and reported marginal improvements in accuracy. In this study we describe and evaluate a 3D end-to-end hybrid MAS and DCNN segmentation pipeline, called Deep Label Fusion (DLF). The DLF pipeline consists of two main components with learnable weights, including a weighted voting subnet that mimics the MAS algorithm and a fine-tuning subnet that corrects residual segmentation errors to improve final segmentation accuracy. We evaluate DLF on five datasets that represent a diversity of anatomical structures (medial temporal lobe subregions and lumbar vertebrae) and imaging modalities (multi-modality, multi-field-strength MRI and Computational Tomography). These experiments show that DLF achieves comparable segmentation accuracy to nnU-Net (Isensee et al., 2020), the state-of-the-art DCNN pipeline, when evaluated on a dataset with similar characteristics to the training datasets, while outperforming nnU-Net on tasks that involve generalization to datasets with different characteristics (different MRI field strength or different patient population). DLF is also shown to consistently improve upon conventional MAS methods. In addition, a modality augmentation strategy tailored for multimodal imaging is proposed and demonstrated to be beneficial in improving the segmentation accuracy of learning-based methods, including DLF and DCNN, in missing data scenarios in test time as well as increasing the interpretability of the contribution of each individual modality.

Cortical thickness of primary motor and vestibular brain regions predicts recovery from fall and balance directly after spaceflight

Motor adaptations to the microgravity environment during spaceflight allow astronauts to perform adequately in this unique environment. Upon return to Earth, this adaptation is no longer appropriate and can be disruptive for mission critical tasks. Here, we measured if metrics derived from MRI scans collected from astronauts can predict motor performance post-flight. Structural and diffusion MRI scans from 14 astronauts collected before launch, and motor measures (balance performance, speed of recovery from fall, and tandem walk step accuracy) collected pre-flight and post-flight were analyzed. Regional measures of gray matter volume (motor cortex, paracentral lobule, cerebellum), myelin density (motor cortex, paracentral lobule, corticospinal tract), and white matter microstructure (corticospinal tract) were derived as a-priori predictors. Additional whole-brain analyses of cortical thickness, cerebellar gray matter, and cortical myelin were also tested for associations with post-flight and pre-to-post-flight motor performance. The pre-selected regional measures were not significantly associated with motor behavior. However, whole-brain analyses showed that paracentral and precentral gyri thickness significantly predicted recovery from fall post-spaceflight. Thickness of vestibular and sensorimotor regions, including the posterior insula and the superior temporal gyrus, predicted balance performance post-flight and pre-to-post-flight decrements. Greater cortical thickness pre-flight predicted better performance post-flight. Regional thickness of somatosensory, motor, and vestibular brain regions has some predictive value for post-flight motor performance in astronauts, which may be used for the identification of training and countermeasure strategies targeted for maintaining operational task performance.

Growth charts of brain morphometry for preschool children

Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a total sample size of 285, we characterized the age-dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age-dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity.

Mothers’ Attachment Representations and Children’s Brain Structure

Ample research demonstrates that parents’ experience-based mental representations of attachment—cognitive models of close relationships—relate to their children’s social-emotional development. However, no research to date has examined how parents’ attachment representations relate to another crucial domain of children’s development: brain development. The present study is the first to integrate the separate literatures on attachment and developmental social cognitive neuroscience to examine the link between mothers’ attachment representations and 3- to 8-year-old children’s brain structure. We hypothesized that mothers’ attachment representations would relate to individual differences in children’s brain structures involved in stress regulation—specifically, amygdala and hippocampal volumes—in part via mothers’ responses to children’s distress. We assessed 52 mothers’ attachment representations (secure base script knowledge on the Attachment Script Assessment and self-reported attachment avoidance and anxiety on the Experiences in Close Relationships scale) and children’s brain structure. Mothers’ secure base script knowledge was significantly related to children’s smaller left amygdala volume but was unrelated to hippocampal volume; we found no indirect links via maternal responses to children’s distress. Exploratory analyses showed associations between mothers’ attachment representations and white matter and thalamus volumes. Together, these preliminary results suggest that mothers’ attachment representations may be linked to the development of children’s neural circuitry related to stress regulation.

Automatic Segmentation of Bone Selective MR Images for Visualization and Craniometry of the Cranial Vault

RATIONALE AND OBJECTIVES

Solid-state MRI has been shown to provide a radiation-free alternative imaging strategy to CT. However, manual image segmentation to produce bone-selective MR-based 3D renderings is time and labor intensive, thereby acting as a bottleneck in clinical practice. The objective of this study was to evaluate an automatic multi-atlas segmentation pipeline for use on cranial vault images entirely circumventing prior manual intervention, and to assess concordance of craniometric measurements between pipeline produced MRI and CT-based 3D skull renderings.

MATERIALS AND METHODS

Dual-RF, dual-echo, 3D UTE pulse sequence MR data were obtained at 3T on 30 healthy subjects along with low-dose CT images between December 2018 to January 2020 for this prospective study. The four-point MRI datasets (two RF pulse widths and two echo times) were combined to produce bone-specific images. CT images were thresholded and manually corrected to segment the cranial vault. CT images were then rigidly registered to MRI using mutual information. The corresponding cranial vault segmentations were then transformed to MRI. The "ground truth" segmentations served as reference for the MR images. Subsequently, an automated multi-atlas pipeline was used to segment the bone-selective images. To compare manually and automatically segmented MR images, the Dice similarity coefficient (DSC) and Hausdorff distance (HD) were computed, and craniometric measurements between CT and automated-pipeline MRI-based segmentations were examined via Lin's concordance coefficient (LCC).

RESULTS

Automated segmentation reduced the need for an expert to obtain segmentation. Average DSC was 90.86 ± 1.94%, and average 95th percentile HD was 1.65 ± 0.44 mm between ground truth and automated segmentations. MR-based measurements differed from CT-based measurements by 0.73-1.2 mm on key craniometric measurements. LCC for distances between CT and MR-based landmarks were vertex-basion: 0.906, left-right frontozygomatic suture: 0.780, and glabella-opisthocranium: 0.956 for the three measurements.

CONCLUSION

Good agreement between CT and automated MR-based 3D cranial vault renderings has been achieved, thereby eliminating the laborious manual segmentation process. Target applications comprise craniofacial surgery as well as imaging of traumatic injuries and masses involving both bone and soft tissue.

Clinical and Molecular Correlates of Abnormal Changes in the Cerebellum and Globus Pallidus in Fragile X Premutation

BACKGROUND

Fragile X premutation carriers (55-200 CGG triplets) may develop a progressive neurodegenerative disorder, fragile X-associated tremor/ataxia syndrome (FXTAS), after the age of 50. The neuroradiologic markers of FXTAS are hyperintense T2-signals in the middle cerebellar peduncle-the MCP sign. We recently noticed abnormal T2-signals in the globus pallidus in male premutation carriers and controls but the prevalence and clinical significance were unknown.

METHODS

We estimated the prevalence of the MCP sign and pallidal T2-abnormalities in 230 male premutation carriers and 144 controls (aged 8-86), and examined the associations with FXTAS symptoms, CGG repeat length, and iron content in the cerebellar dentate nucleus and globus pallidus.

RESULTS

Among participants aged ≥45 years (175 premutation carriers and 82 controls), MCP sign was observed only in premutation carriers (52 vs. 0%) whereas the prevalence of pallidal T2-abnormalities approached significance in premutation carriers compared with controls after age-adjustment (25.1 vs. 13.4%, p = 0.069). MCP sign was associated with impaired motor and executive functioning, and the additional presence of pallidal T2-abnormalities was associated with greater impaired executive functioning. Among premutation carriers, significant iron accumulation was observed in the dentate nucleus, and neither pallidal or MCP T2-abnormalities affected measures of the dentate nucleus. While the MCP sign was associated with CGG repeat length >75 and dentate nucleus volume correlated negatively with CGG repeat length, pallidal T2-abnormalities did not correlate with CGG repeat length. However, pallidal signal changes were associated with age-related accelerated iron depletion and variability and having both MCP and pallidal signs further increased iron variability in the globus pallidus.

CONCLUSIONS

Only the MCP sign, not pallidal abnormalities, revealed independent associations with motor and cognitive impairment; however, the occurrence of combined MCP and pallidal T2-abnormalities may present a risk for greater cognitive impairment and increased iron variability in the globus pallidus.

Factorisation-Based Image Labelling

Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging. Manual annotation is time consuming and expensive, so having a fully automated and general purpose brain segmentation algorithm is highly desirable. To this end, we propose a patched-based labell propagation approach based on a generative model with latent variables. Once trained, our Factorisation-based Image Labelling (FIL) model is able to label target images with a variety of image contrasts. We compare the effectiveness of our proposed model against the state-of-the-art using data from the MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labelling. As our approach is intended to be general purpose, we also assess how well it can handle domain shift by labelling images of the same subjects acquired with different MR contrasts.

Agreement Between Automated and Manual MRI Volumetry in Alzheimer's Disease: A Systematic Review and Meta-Analysis

BACKGROUND

Automated magnetic resonance imaging (MRI) volumetry is a promising tool to evaluate regional brain volumes in dementia and especially Alzheimer's disease (AD).

PURPOSE

To compare automated methods and the gold standard manual segmentation in measuring regional brain volumes on MRI across healthy controls, patients with mild cognitive impairment, and patients with dementia due to AD.

STUDY TYPE

Systematic review and meta-analysis.

DATA SOURCES

MEDLINE, Embase, and PsycINFO were searched through October 2021.

FIELD STRENGTH

1.0 T, 1.5 T, or 3.0 T.

ASSESSMENT

Two review authors independently identified studies for inclusion and extracted data. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2).

STATISTICAL TESTS

Standardized mean differences (SMD; Hedges' g) were pooled using random-effects meta-analysis with robust variance estimation. Subgroup analyses were undertaken to explore potential sources of heterogeneity. Sensitivity analyses were conducted to examine the impact of the within-study correlation between effect estimates on the meta-analysis results.

RESULTS

Seventeen studies provided sufficient data to evaluate the hippocampus, lateral ventricles, and parahippocampal gyrus. The pooled SMD for the hippocampus, lateral ventricles, and parahippocampal gyrus were 0.22 (95% CI -0.50 to 0.93), 0.12 (95% CI -0.13 to 0.37), and -0.48 (95% CI -1.37 to 0.41), respectively. For the hippocampal data, subgroup analyses suggested that the pooled SMD was invariant across clinical diagnosis and field strength. Subgroup analyses could not be conducted on the lateral ventricles data and the parahippocampal gyrus data due to insufficient data. The results were robust to the selected within-study correlation value.

DATA CONCLUSION

While automated methods are generally comparable to manual segmentation for measuring hippocampal, lateral ventricle, and parahippocampal gyrus volumes, wide 95% CIs and large heterogeneity suggest that there is substantial uncontrolled variance. Thus, automated methods may be used to measure these regions in patients with AD but should be used with caution.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 3.

Activation for newly learned words in left medial-temporal lobe during toddlers' sleep is associated with memory for words

Little is known about the neural substrates underlying early memory functioning. To gain more insight, we examined how toddlers remember newly learned words. Hippocampal and anterior medial-temporal lobe (MTL) processes have been hypothesized to support forming and retaining the association between novel words and their referents, but direct evidence of this connection in early childhood is lacking. We assessed 2-year-olds (n = 38) for their memory of newly learned pseudowords associated with novel objects and puppets. We tested memory for these associations during the same session as learning and after a 1-week delay. We then played these pseudowords, previously known words, and completely novel pseudowords during natural nocturnal sleep, while collecting functional magnetic resonance imaging data. Activation in the left hippocampus and the left anterior MTL for newly learned compared to novel words was associated with same-session memory for these newly learned words only when they were learned as puppet names. Activation for known words was associated with memory for puppet names at the 1-week delay. Activation for newly learned words was also associated with overall productive vocabulary. These results underscore an early developing link between memory mechanisms and word learning in the medial temporal lobe.

Automated, open-source segmentation of the Hippocampus and amygdala with the open Vanderbilt archive of the temporal lobe

Examining volumetric differences of the amygdala and anterior-posterior regions of the hippocampus is important for understanding cognition and clinical disorders. However, the gold standard manual segmentation of these structures is time and labor-intensive. Automated, accurate, and reproducible techniques to segment the hippocampus and amygdala are desirable. Here, we present a hierarchical approach to multi-atlas segmentation of the hippocampus head, body and tail and the amygdala based on atlases from 195 individuals. The Open Vanderbilt Archive of the temporal Lobe (OVAL) segmentation technique outperforms the commonly used FreeSurfer, FSL FIRST, and whole-brain multi-atlas segmentation approaches for the full hippocampus and amygdala and nears or exceeds inter-rater reproducibility for segmentation of the hippocampus head, body and tail. OVAL has been released in open-source and is freely available.

Whole-brain functional ultrasound imaging in awake head-fixed mice

Most brain functions engage a network of distributed regions. Full investigation of these functions thus requires assessment of whole brains; however, whole-brain functional imaging of behaving animals remains challenging. This protocol describes how to follow brain-wide activity in awake head-fixed mice using functional ultrasound imaging, a method that tracks cerebral blood volume dynamics. We describe how to set up a functional ultrasound imaging system with a provided acquisition software (miniScan), establish a chronic cranial window (timing surgery: ~3-4 h) and image brain-wide activity associated with a stimulus at high resolution (100 × 110 × 300 µm and 10 Hz per brain slice, which takes ~45 min per imaging session). We include codes that enable data to be registered to a reference atlas, production of 3D activity maps, extraction of the activity traces of ~250 brain regions and, finally, combination of data from multiple sessions (timing analysis averages ~2 h). This protocol enables neuroscientists to observe global brain processes in mice.

Hippocampal subregion volume in high-risk offspring is associated with increases in depressive symptoms across the transition to adolescence

BACKGROUND

The hippocampus has been implicated in the pathophysiology of depression. This study examined whether youth hippocampal subregion volumes were differentially associated with maternal depression history and youth's depressive symptoms across the transition to adolescence.

METHODS

74 preadolescent offspring (M_age=10.74+/-0.84 years) of mothers with (n = 33) and without a lifetime depression history (n = 41) completed a structural brain scan. Youth depressive symptoms were assessed with clinical interviews and mother- and youth-reports prior to the neuroimaging assessment at age 9 (M_age=9.08+/-0.29 years), at the neuroimaging assessment, and in early adolescence (M_age=12.56+/-0.40 years).

RESULTS

Maternal depression was associated with preadolescent offspring's reduced bilateral hippocampal head volumes and increased left hippocampal body volume. Reduced bilateral head volumes were associated with offspring's increased concurrent depressive symptoms. Furthermore, reduced right hippocampal head volume mediated associations between maternal depression and increases in offspring depressive symptoms from age 9 to age 12.

LIMITATIONS

This study included a modest-sized sample that was oversampled for early temperamental characteristics, one neuroimaging assessment, and no correction for multiple comparisons.

CONCLUSIONS

Findings implicate reductions in hippocampal head volume in the intergenerational transmission of risk from parents to offspring.

How Behavior Shapes the Brain and the Brain Shapes Behavior: Insights from Memory Development

Source memory improves substantially during childhood. This improvement is thought to be closely related to hippocampal maturation. As previous studies have mainly used cross-sectional designs to assess relations between source memory and hippocampal function, it remains unknown whether changes in the brain precede improvements in memory or vice versa. To address this gap, the current study used an accelerated longitudinal design (n = 200, 100 males) to follow 4- and 6-year-old human children for 3 years. We traced developmental changes in source memory and intrinsic hippocampal functional connectivity and assessed differences between the 4- and 6-year-old cohorts in the predictive relations between source memory changes and intrinsic hippocampal functional connectivity in the absence of a demanding task. Consistent with previous studies, there were age-related increases in source memory and intrinsic functional connectivity between the hippocampus and cortical regions known to be involved during memory encoding. Novel findings showed that changes in memory ability early in life predicted later connectivity between the hippocampus and cortical regions and that intrinsic hippocampal functional connectivity predicted later changes in source memory. These findings suggest that behavioral experience and brain development are interactive, bidirectional processes, such that experience shapes future changes in the brain and the brain shapes future changes in behavior. Results also suggest that both timing and location matter, as the observed effects depended on both children's age and the specific brain ROIs. Together, these findings add critical insight into the interactive relations between cognitive processes and their underlying neurologic bases during development.SIGNIFICANCE STATEMENT Cross-sectional studies have shown that the ability to remember the contextual details of previous experiences (i.e., source memory) is related to hippocampal development in childhood. It is unknown whether hippocampal functional changes precede improvements in memory or vice versa. By using an accelerated longitudinal design, we found that early source memory changes predicted later intrinsic hippocampal functional connectivity and that this connectivity predicted later source memory changes. These findings suggest that behavioral experience and brain development are interactive, bidirectional processes, such that experience shapes future changes in the brain and the brain shapes future behavioral changes. Moreover, these interactions varied as a function of children's age and brain region, highlighting the importance of a developmental perspective when investigating brain-behavior interactions.

Virtual brain grafting: Enabling whole brain parcellation in the presence of large lesions

Brain atlases and templates are at the heart of neuroimaging analyses, for which they facilitate multimodal registration, enable group comparisons and provide anatomical reference. However, as atlas-based approaches rely on correspondence mapping between images they perform poorly in the presence of structural pathology. Whilst several strategies exist to overcome this problem, their performance is often dependent on the type, size and homogeneity of any lesions present. We therefore propose a new solution, referred to as Virtual Brain Grafting (VBG), which is a fully-automated, open-source workflow to reliably parcellate magnetic resonance imaging (MRI) datasets in the presence of a broad spectrum of focal brain pathologies, including large, bilateral, intra- and extra-axial, heterogeneous lesions with and without mass effect. The core of the VBG approach is the generation of a lesion-free T1-weighted image, which enables further image processing operations that would otherwise fail. Here we validated our solution based on Freesurfer recon-all parcellation in a group of 10 patients with heterogeneous gliomatous lesions, and a realistic synthetic cohort of glioma patients (n = 100) derived from healthy control data and patient data. We demonstrate that VBG outperforms a non-VBG approach assessed qualitatively by expert neuroradiologists and Mann-Whitney U tests to compare corresponding parcellations (real patients U(6,6) = 33, z = 2.738, P < .010, synthetic-patients U(48,48) = 2076, z = 7.336, P < .001). Results were also quantitatively evaluated by comparing mean dice scores from the synthetic-patients using one-way ANOVA (unilateral VBG = 0.894, bilateral VBG = 0.903, and non-VBG = 0.617, P < .001). Additionally, we used linear regression to show the influence of lesion volume, lesion overlap with, and distance from the Freesurfer volumes of interest, on labeling accuracy. VBG may benefit the neuroimaging community by enabling automated state-of-the-art MRI analyses in clinical populations using methods such as FreeSurfer, CAT12, SPM, Connectome Workbench, as well as structural and functional connectomics. To fully maximize its availability, VBG is provided as open software under a Mozilla 2.0 license (https://github.com/KUL-Radneuron/KUL_VBG).

Multi-scale graph-based grading for Alzheimer's disease prediction

The prediction of subjects with mild cognitive impairment (MCI) who will progress to Alzheimer's disease (AD) is clinically relevant, and may above all have a significant impact on accelerating the development of new treatments. In this paper, we present a new MRI-based biomarker that enables us to accurately predict conversion of MCI subjects to AD. In order to better capture the AD signature, we introduce two main contributions. First, we present a new graph-based grading framework to combine inter-subject similarity features and intra-subject variability features. This framework involves patch-based grading of anatomical structures and graph-based modeling of structure alteration relationships. Second, we propose an innovative multiscale brain analysis to capture alterations caused by AD at different anatomical levels. Based on a cascade of classifiers, this multiscale approach enables the analysis of alterations of whole brain structures and hippocampus subfields at the same time. During our experiments using the ADNI-1 dataset, the proposed multiscale graph-based grading method obtained an area under the curve (AUC) of 81% to predict conversion of MCI subjects to AD within three years. Moreover, when combined with cognitive scores, the proposed method obtained 85% of AUC. These results are competitive in comparison to state-of-the-art methods evaluated on the same dataset.

Pathomechanisms of HIV-Associated Cerebral Small Vessel Disease: A Comprehensive Clinical and Neuroimaging Protocol and Analysis Pipeline

Rationale: We provide an in-depth description of a comprehensive clinical, immunological, and neuroimaging study that includes a full image processing pipeline. This approach, although implemented in HIV infected individuals, can be used in the general population to assess cerebrovascular health.

Aims: In this longitudinal study, we seek to determine the effects of neuroinflammation due to HIV-1 infection on the pathomechanisms of cerebral small vessel disease (CSVD). The study focuses on the interaction of activated platelets, pro-inflammatory monocytes and endothelial cells and their impact on the neurovascular unit. The effects on the neurovascular unit are evaluated by a novel combination of imaging biomarkers.

Sample Size: We will enroll 110 HIV-infected individuals on stable combination anti-retroviral therapy for at least three months and an equal number of age-matched controls. We anticipate a drop-out rate of 20%.

Methods and Design: Subjects are followed for three years and evaluated by flow cytometric analysis of whole blood (to measure platelet activation, platelet monocyte complexes, and markers of monocyte activation), neuropsychological testing, and brain MRI at the baseline, 18- and 36-month time points. MRI imaging follows the recommended clinical small vessel imaging standards and adds several advanced sequences to obtain quantitative assessments of brain tissues including white matter microstructure, tissue susceptibility, and blood perfusion.

Discussion: The study provides further understanding of the underlying mechanisms of CSVD in chronic inflammatory disorders such as HIV infection. The longitudinal study design and comprehensive approach allows the investigation of quantitative changes in imaging metrics and their impact on cognitive performance.

Volumetric GWAS of medial temporal lobe structures identifies an ERC1 locus using ADNI high-resolution T2-weighted MRI data

Medial temporal lobe (MTL) consists of hippocampal subfields and neighboring cortices. These heterogeneous structures are differentially involved in memory, cognitive and emotional functions, and present nonuniformly distributed atrophy contributing to cognitive disorders. This study aims to examine how genetics influences Alzheimer's disease (AD) pathogenesis via MTL substructures by analyzing high-resolution magnetic resonance imaging (MRI) data. We performed genome-wide association study to examine the associations between 565,373 single nucleotide polymorphisms (SNPs) and 14 MTL substructure volumes. A novel association with right Brodmann area 36 volume was discovered in an ERC1 SNP (i.e., rs2968869). Further analyses on larger samples found rs2968869 to be associated with gray matter density and glucose metabolism measures in the right hippocampus, and disease status. Tissue-specific transcriptomic analysis identified the minor allele of rs2968869 (rs2968869-C) to be associated with reduced ERC1 expression in the hippocampus. All the findings indicated a protective role of rs2968869-C in AD. We demonstrated the power of high-resolution MRI and the promise of fine-grained MTL substructures for revealing the genetic basis of AD biomarkers.

Inter-method reliability between automatic region of interest analytic application with multi-atlas segmentation and FreeSurfer

AIM

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the aggregation of amyloid-β and phosphorylated tau proteins. Magnetic resonance imaging (MRI) is a useful means of detecting hippocampal atrophy. However, instead of visual inspection, objective and time-saving tools for automated region of interest (ROI) analysis are needed. Advances in MRI segmentation techniques have enabled a multi-atlas approach with fewer errors than a conventional single-atlas approach. To support the clinical application of multi-atlas segmentation, an automated ROI analytic application consisting of multi-atlas segmentation with joint label fusion and corrective learning was developed: T-Proto. In the present study, we evaluated the inter-method reliability between T-Proto and a reference ROI analytic software, FreeSurfer.

METHODS

This was a database study. MRI data from 30 patients with AD were selected, and the inter-method reliability was assessed in terms of the intra-class correlation coefficient (ICC). A post-hoc comparison according to the severity of AD was also performed.

RESULTS

Almost all the regional volumes estimated with T-Proto were smaller than those estimated with FreeSurfer. The regional ICC values between the two methods showed moderate to excellent reliability. A post-hoc comparison revealed a similar t-value and effect size between both methods for the hippocampus.

CONCLUSION

In the present study, we showed that automated regional analysis using T-Proto was reliable in the hippocampus in terms of ICC, compared with FreeSurfer.

Volume increase in the dentate gyrus after electroconvulsive therapy in depressed patients as measured with 7T

Electroconvulsive therapy (ECT) is the most effective treatment for depression, yet its working mechanism remains unclear. In the animal analog of ECT, neurogenesis in the dentate gyrus (DG) of the hippocampus is observed. In humans, volume increase of the hippocampus has been reported, but accurately measuring the volume of subfields is limited with common MRI protocols. If the volume increase of the hippocampus in humans is attributable to neurogenesis, it is expected to be exclusively present in the DG, whereas other processes (angiogenesis, synaptogenesis) also affect other subfields. Therefore, we acquired an optimized MRI scan at 7-tesla field strength allowing sensitive investigation of hippocampal subfields. A further increase in sensitivity of the within-subjects measurements is gained by automatic placement of the field of view. Patients receive two MRI scans: at baseline and after ten bilateral ECT sessions (corresponding to a 5-week interval). Matched controls are also scanned twice, with a similar 5-week interval. A total of 31 participants (23 patients, 8 controls) completed the study. A large and significant increase in DG volume was observed after ECT (M = 75.44 mm³, std error = 9.65, p < 0.001), while other hippocampal subfields were unaffected. We note that possible type II errors may be present due to the small sample size. In controls no changes in volume were found. Furthermore, an increase in DG volume was related to a decrease in depression scores, and baseline DG volume predicted clinical response. These findings suggest that the volume change of the DG is related to the antidepressant properties of ECT, and may reflect neurogenesis.

Longitudinal development of hippocampal subregions from early- to mid-childhood

Early childhood is characterized by vast changes in behaviors supported by the hippocampus and an increased susceptibility of the hippocampus to environmental influences. Thus, it is an important time to investigate the development of the hippocampus. Existing research suggests subregions of the hippocampus (i.e., head, body, tail) have dissociable functions and that the relations between subregions and cognitive abilities vary across development. However, longitudinal research examining age-related changes in subregions in humans, particularly during early childhood (i.e., 4-6 years), is limited. Using a large sample of 184 healthy 4- to 8-year-old children, the present study is the first to characterize developmental changes in hippocampal subregion volume from early- to mid-childhood. Results reveal differential developmental trajectories in hippocampal head, body, and tail during this period. Specifically, head volume showed a quadratic pattern of change, and both body and tail showed linear increases, resulting in a pattern of cubic change for total hippocampal volume. Further, main effects of sex on hippocampal volume (males > females) and hemispheric differences in developmental trajectories were observed. These findings provide an improved understanding of the development of the hippocampus and have important implications for research investigating a range of cognitive abilities and behaviors.

Fast and Precise Hippocampus Segmentation Through Deep Convolutional Neural Network Ensembles and Transfer Learning

Automatic segmentation of the hippocampus from 3D magnetic resonance imaging mostly relied on multi-atlas registration methods. In this work, we exploit recent advances in deep learning to design and implement a fully automatic segmentation method, offering both superior accuracy and fast result. The proposed method is based on deep Convolutional Neural Networks (CNNs) and incorporates distinct segmentation and error correction steps. Segmentation masks are produced by an ensemble of three independent models, operating with orthogonal slices of the input volume, while erroneous labels are subsequently corrected by a combination of Replace and Refine networks. We explore different training approaches and demonstrate how, in CNN-based segmentation, multiple datasets can be effectively combined through transfer learning techniques, allowing for improved segmentation quality. The proposed method was evaluated using two different public datasets and compared favorably to existing methodologies. In the EADC-ADNI HarP dataset, the correspondence between the method's output and the available ground truth manual tracings yielded a mean Dice value of 0.9015, while the required segmentation time for an entire MRI volume was 14.8 seconds. In the MICCAI dataset, the mean Dice value increased to 0.8835 through transfer learning from the larger EADC-ADNI HarP dataset.

Using Virtual Reality to Improve Performance and User Experience in Manual Correction of MRI Segmentation Errors by Non-experts

Segmentation of MRI scans is a critical part of the workflow process before we can further analyze neuroimaging data. Although there are several automatic tools for segmentation, no segmentation software is perfectly accurate, and manual correction by visually inspecting the segmentation errors is required. The process of correcting these errors is tedious and time-consuming, so we present a novel method of performing this task in a head-mounted virtual reality interactive system with a new software, Virtual Brain Segmenter (VBS). We provide the results of user testing on 30 volunteers to show the benefits of our tool as a more efficient, intuitive, and engaging alternative compared with the current method of correcting segmentation errors.

Rapidly Progressing Neurocognitive Disorder in a Male with FXTAS and Alzheimer's Disease

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder that usually begins in the early 60s and affects carriers of premutation expansion (55-200 CGG repeats) of the fragile X mental retardation 1 (FMR1) gene. Additional disorders can co-occur with FXTAS including Alzheimer's disease (AD). Here we discuss a case report of a male with 67 CGG repeats in FMR1 who had mild late-onset FXTAS symptoms followed by neurocognitive disorder symptoms consistent with AD. The patient has developed tremor and ataxia that are the two characteristic symptoms of FXTAS. In addition, he shows rapid cognitive decline, brain atrophy most substantial in the medial temporal lobe, and decreased metabolism in the brain regions that are the characteristic findings of AD. The purpose of this study is to describe a patient profile with both diseases and review the details of an overlap between these two diseases.

Minimally interactive placenta segmentation from three-dimensional ultrasound images

Purpose: Placental size in early pregnancy has been associated with important clinical outcomes, including fetal growth. However, extraction of placental size from three-dimensional ultrasound (3DUS) requires time-consuming interactive segmentation methods and is prone to user variability. We propose a semiautomated segmentation technique that requires minimal user input to robustly measure placental volume from 3DUS images. Approach: For semiautomated segmentation, a single, central 2D slice was manually annotated to initialize an automated multi-atlas label fusion (MALF) algorithm. The dataset consisted of 47 3DUS volumes obtained at 11 to 14 weeks in singleton pregnancies (28 anterior and 19 posterior). Twenty-six of these subjects were imaged twice within the same session. Dice overlap and surface distance were used to quantify the automated segmentation accuracy compared to expert manual segmentations. The mean placental volume measurements obtained by our method and VOCAL (virtual organ computer-aided analysis), a leading commercial semiautomated method, were compared to the manual reference set. The test-retest reliability was also assessed. Results: The overlap between our automated segmentation and manual (mean Dice: 0.824 ± 0.061 , median: 0.831) was within the range reported by other methods requiring extensive manual input. The average surface distance was 1.66 ± 0.96    mm . The correlation coefficient between test-retest volumes was r = 0.88 , and the intraclass correlation was ICC ( 1 ) = 0.86 . Conclusions: MALF is a promising method that can allow accurate and reliable segmentation of the placenta with minimal user interaction. Further refinement of this technique may allow for placental biometry to be incorporated into clinical pregnancy surveillance.

Empathic responding and hippocampal volume in young children

Empathic responding-the capacity to understand, resonate with, and respond sensitively to others' emotional experiences-is a complex human faculty that calls upon multiple social, emotional, and cognitive capacities and their underlying neural systems. Emerging evidence in adults has suggested that the hippocampus and its associated network may play an important role in empathic responding, possibly via processes such as memory of emotional events, but the contribution of this structure in early childhood is unknown. We examined concurrent associations between empathic responding and hippocampal volume in a sample of 78 children (ages 4-8 years). Larger bilateral hippocampal volume (adjusted for intracranial volume) predicted greater observed empathic responses toward an experimenter in distress, but only for boys. The association was not driven by a specific subregion of the hippocampus (head, body, tail), nor did it vary with age. Empathic responding was not significantly related to amygdala volume, suggesting specificity of relations with the hippocampus. Results support the proposal that hippocampal structure contributes to individual differences in children's empathic responding, consistent with research in adults. Findings shed light on an understudied structure in the complex neural systems supporting empathic responding and raise new questions regarding sex differences in the neurodevelopment of empathy in early childhood. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Interaction between ventricular expansion and structural changes in the corpus callosum and putamen in males with FMR1 normal and premutation alleles

Ventricular enlargement (VE) is commonly observed in aging and fragile X-associated tremor/ataxia syndrome (FXTAS), a late-onset neurodegenerative disorder. VE may generate a mechanical force causing structural deformation. In this longitudinal study, we examined the relationships between VE and structural changes in the corpus callosum (CC) and putamen. MRI scans (2-7/person over 0.2-7.5 years) were acquired from 22 healthy controls, 26 unaffected premutation carriers (PFX-), and 39 carriers affected with FXTAS (PFX+). Compared with controls, PFX- demonstrated enlarged fourth ventricles, whereas PFX+ displayed enlargement in both third and fourth ventricles, CC thinning, putamen atrophy/deformation (thinning and increased distance), and accelerated expansions in lateral ventricles. Common for all groups, baseline VE predicted accelerated CC thinning and putamen atrophy/deformation and conversely, baseline CC and putamen atrophy/deformation and enlarged third and fourth ventricles predicted accelerated lateral ventricular expansion. The results suggest a progressive VE within the 4 ventricles as FXTAS develops and a deleterious cycle between VE and brain deformation that may commonly occur during aging and FXTAS progression but become accelerated in FXTAS.

Developmental aspects of FXAND in a man with the FMR1 premutation

BACKGROUND

Fragile X mental retardation 1 (FMR1) premutation can cause developmental problems including autism spectrum disorder (ASD), social anxiety, depression, and attention deficit hyperactivity disorder (ADHD). These problems fall under an umbrella term of Fragile X-associated Neuropsychiatric Disorders (FXAND) and is separate from Fragile X-associated Tremor/Ataxia syndrome (FXTAS), a neurodegenerative disorder.

METHODS/CLINICAL CASE

A 26-year-old Caucasian male with the Fragile X premutation who presented with multiple behavior and emotional problems including depression and anxiety at 10 years of age. He was evaluated at 13, 18, and 26 years old with age-appropriate cognitive assessments, psychiatric evaluations, and an MRI of the brain.

RESULTS

The Autism Diagnostic Observation Scale (ADOS) was done at 13 years old and showed the patient has autism spectrum disorder (ASD). An evaluation at 18 years old showed a full-scale IQ of 64. A Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS) performed at 26 years old confirmed the previous impression of social anxiety disorder, agoraphobia disorder, and selective mutism. His MRI acquired at 26 years old showed enlarged ventricles, increased frontal subarachnoid spaces, and hypergyrification.

CONCLUSION

This is an exemplary case of an FMR1 premutation carrier with significant psychiatric and cognitive issues that demonstrates Fragile X-associated Neuropsychiatric Disorders (FXAND) as separate from the other well-known premutation disorders.

Accurate and robust segmentation of neuroanatomy in T1-weighted MRI by combining spatial priors with deep convolutional neural networks

Neuroanatomical segmentation in magnetic resonance imaging (MRI) of the brain is a prerequisite for quantitative volume, thickness, and shape measurements, as well as an important intermediate step in many preprocessing pipelines. This work introduces a new highly accurate and versatile method based on 3D convolutional neural networks for the automatic segmentation of neuroanatomy in T1‐weighted MRI. In combination with a deep 3D fully convolutional architecture, efficient linear registration‐derived spatial priors are used to incorporate additional spatial context into the network. An aggressive data augmentation scheme using random elastic deformations is also used to regularize the networks, allowing for excellent performance even in cases where only limited labeled training data are available. Applied to hippocampus segmentation in an elderly population (mean Dice coefficient = 92.1%) and subcortical segmentation in a healthy adult population (mean Dice coefficient = 89.5%), we demonstrate new state‐of‐the‐art accuracies and a high robustness to outliers. Further validation on a multistructure segmentation task in a scan–rescan dataset demonstrates accuracy (mean Dice coefficient = 86.6%) similar to the scan–rescan reliability of expert manual segmentations (mean Dice coefficient = 86.9%), and improved reliability compared to both expert manual segmentations and automated segmentations using FIRST. Furthermore, our method maintains a highly competitive runtime performance (e.g., requiring only 10 s for left/right hippocampal segmentation in 1 × 1 × 1 mm³ MNI stereotaxic space), orders of magnitude faster than conventional multiatlas segmentation methods.

Automated Multi-Atlas Segmentation of Hippocampal and Extrahippocampal Subregions in Alzheimer's Disease at 3T and 7T: What Atlas Composition Works Best?

BACKGROUND

Multi-atlas segmentation, a popular technique implemented in the Automated Segmentation of Hippocampal Subfields (ASHS) software, utilizes multiple expert-labelled images ("atlases") to delineate medial temporal lobe substructures. This multi-atlas method is increasingly being employed in early Alzheimer's disease (AD) research, it is therefore becoming important to know how the construction of the atlas set in terms of proportions of controls and patients with mild cognitive impairment (MCI) and/or AD affects segmentation accuracy.

OBJECTIVE

To evaluate whether the proportion of controls in the training sets affects the segmentation accuracy of both controls and patients with MCI and/or early AD at 3T and 7T.

METHODS

We performed cross-validation experiments varying the proportion of control subjects in the training set, ranging from a patient-only to a control-only set. Segmentation accuracy of the test set was evaluated by the Dice similarity coeffiecient (DSC). A two-stage statistical analysis was applied to determine whether atlas composition is linked to segmentation accuracy in control subjects and patients, for 3T and 7T.

RESULTS

The different atlas compositions did not significantly affect segmentation accuracy at 3T and for patients at 7T. For controls at 7T, including more control subjects in the training set significantly improves the segmentation accuracy, but only marginally, with the maximum of 0.0003 DSC improvement per percent increment of control subject in the training set.

CONCLUSION

ASHS is robust in this study, and the results indicate that future studies investigating hippocampal subfields in early AD populations can be flexible in the selection of their atlas compositions.

Multiseg pipeline: automatic tissue segmentation of brain MR images with subject-specific atlases

Automated segmentation and labeling of individual brain anatomical regions is challenging due to individual structural variability. Although, atlas-based segmentation has shown its potential for both tissue and structure segmentation, the inherent natural variability as well as disease-related changes in MR appearance is often inappropriately represented by a single atlas image. In order to have a more accurate representation, several atlases may be used for the segmentation task in a given neuroimaging study. In this paper, we present the MultisegPipeline, it uses multiple atlases that have been visually inspected and capture the expected variability in a neonatal population. The MultisegPipeline transfers the labeled regions from each atlas to the target image using deformable registration (ANTs¹ or QuickSilver² is available for this task). Additionally, the set of labels are merged using a label fusion technique that reduces the errors produced by the registration. The final output is a single label map that combines the results produced by all atlases into a consensus solution. In our study, the MultisegPipeline is used to segment brain MR images from 31 infants, a leave-one-out strategy was used to test our framework. The average dice score coefficient was 0.89.

Volumetric comparison of hippocampal subfields extracted from 4-minute accelerated vs. 8-minute high-resolution T2-weighted 3T MRI scans

The hippocampus has been widely studied using neuroimaging, as it plays an important role in memory and learning. However, hippocampal subfield information is difficult to capture by standard magnetic resonance imaging (MRI) techniques. To facilitate morphometric study of hippocampal subfields, ADNI introduced a high resolution (0.4 mm in plane) T2-weighted turbo spin-echo sequence that requires 8 min. With acceleration, the protocol can be acquired in 4 min. We performed a comparative study of hippocampal subfield volumes using standard and accelerated protocols on a Siemens Prisma 3T MRI in an independent sample of older adults that included 10 cognitively normal controls, 9 individuals with subjective cognitive decline, 10 with mild cognitive impairment, and 6 with a clinical diagnosis of Alzheimer's disease (AD). The Automatic Segmentation of Hippocampal Subfields (ASHS) software was used to segment 9 primary labeled regions including hippocampal subfields and neighboring cortical regions. Intraclass correlation coefficients were computed for reliability tests between 4 and 8 min scans within and across the four groups. Pairwise group analyses were performed, covaried for age, sex and total intracranial volume, to determine whether the patterns of group differences were similar using 4 vs. 8 min scans. The 4 and 8 min protocols, analyzed by ASHS segmentation, yielded similar volumetric estimates for hippocampal subfields as well as comparable patterns of differences between study groups. The accelerated protocol can provide reliable imaging data for investigation of hippocampal subfields in AD-related MRI studies and the decreased scan time may result in less vulnerability to motion.

Relations between neural structures and children's self-derivation of new knowledge through memory integration

Accumulation of semantic or factual knowledge is a major task during development. Knowledge builds through direct experience and explicit instruction as well as through productive processes that permit derivation of new understandings. In the present research, we tested the neural bases of the specific productive process of self-derivation of new factual knowledge through integration of separate yet related episodes of new learning. The process serves as an ecologically valid model of semantic knowledge accumulation. We tested structure/behavior relations in 5- to 8-year-old children, a period characterized by both age-related differences and individual variability in self-derivation, as well as in the neural regions implicated in memory integration, namely the hippocampus and prefrontal cortex. After controlling for the variance in task performance explained by age, sex, verbal IQ, and gray-matter volume (medial prefrontal cortex, mPFC, only), we observed relations between right mPFC thickness and memory for information explicitly taught to the children as well as the new information they self-derived; relations with the volume of the right hippocampus approached significance. This research provides the first evidence of the neural substrate that subserves children's accumulation of knowledge via self-derivation through memory integration, an empirically demonstrated, functionally significant learning mechanism.

Automated and manual hippocampal segmentation techniques: Comparison of results, reproducibility and clinical applicability

BACKGROUND

Imaging techniques used to measure hippocampal atrophy are key to understanding the clinical progression of Alzheimer's disease (AD). Various semi-automated hippocampal segmentation techniques are available and require human expert input to learn how to accurately segment new data. Our goal was to compare 1) the performance of our automated hippocampal segmentation technique relative to manual segmentations, and 2) the performance of our automated technique when provided with a training set from two different raters. We also explored the ability of hippocampal volumes obtained using manual and automated hippocampal segmentations to predict conversion from MCI to AD.

METHODS

We analyzed 161 1.5 T T1-weighted brain magnetic resonance images (MRI) from the ADCS Donepezil/Vitamin E clinical study. All subjects carried a diagnosis of mild cognitive impairment (MCI). Three different segmentation outputs (one produced by manual tracing and two produced by a semi-automated algorithm trained with training sets developed by two raters) were compared using single measure intraclass correlation statistics (smICC). The radial distance method was used to assess each segmentation technique's ability to detect hippocampal atrophy in 3D. We then compared how well each segmentation method detected baseline hippocampal differences between MCI subjects who remained stable (MCInc) and those who converted to AD (MCIc) during the trial. Our statistical maps were corrected for multiple comparisons using permutation-based statistics with a threshold of p < .01.

RESULTS

Our smICC analyses showed significant agreement between the manual and automated hippocampal segmentations from rater 1 [right smICC = 0.78 (95%CI 0.72-0.84); left smICC = 0.79 (95%CI 0.72-0.85)], the manual segmentations from rater 1 versus the automated segmentations from rater 2 [right smICC = 0.78 (95%CI 0.7-0.84); left smICC = 0.78 (95%CI 0.71-0.84)], and the automated segmentations of rater 1 versus rater 2 [right smICC = 0.97 (95%CI 0.96-0.98); left smICC = 0.97 (95%CI 0.96-0.98)]. All three segmentation methods detected significant CA1 and subicular atrophy in MCIc compared to MCInc at baseline (manual: right pcorrected = 0.0112, left pcorrected = 0.0006; automated rater 1: right pcorrected = 0.0318, left pcorrected = 0.0302; automated rater 2: right pcorrected = 0.0029, left pcorrected = 0.0166).

CONCLUSIONS

The hippocampal volumes obtained with a fast semi-automated segmentation method were highly comparable to the ones obtained with the labor-intensive manual segmentation method. The AdaBoost automated hippocampal segmentation technique is highly reliable allowing the efficient analysis of large data sets.

Molecular physiology of contrast enhancement in glioblastomas: An analysis of The Cancer Imaging Archive (TCIA)

The physiologic processes underlying MRI contrast enhancement in glioblastoma patients remain poorly understood. MRIs of 148 glioblastoma subjects from The Cancer Imaging Archive were segmented using Iterative Probabilistic Voxel Labeling (IPVL). Three aspects of contrast enhancement (CE) were parametrized: the mean intensity of all CE voxels (CEi), the intensity heterogeneity in CE (CEh), and volumetric ratio of CE to necrosis (CEr). Associations between these parameters and patterns of gene expression were analyzed using DAVID functional enrichment analysis. Glioma CpG island methylator phenotype (G-CIMP) glioblastomas were poorly enhancing. Otherwise, no differences in CE parameters were found between proneural, neural, mesenchymal, and classical glioblastomas. High CEi was associated with expression of genes that mediate inflammatory responses. High CEh was associated with increased expression of genes that regulate remodeling of extracellular matrix (ECM) and endothelial permeability. High CEr was associated with increased expression of genes that mediate cellular response to stressful metabolic states, including hypoxia and starvation. Our results indicate that CE in glioblastoma is associated with distinct biological processes involved in inflammatory response and tissue hypoxia. Integrative analysis of these CE parameters may yield meaningful information pertaining to the biologic state of glioblastomas and guide future therapeutic paradigms.

Transfer learning by feature-space transformation: A method for Hippocampus segmentation across scanners

Many successful approaches in MR brain segmentation use supervised voxel classification, which requires manually labeled training images that are representative of the test images to segment. However, the performance of such methods often deteriorates if training and test images are acquired with different scanners or scanning parameters, since this leads to differences in feature representations between training and test data.

In this paper we propose a feature-space transformation (FST) to overcome such differences in feature representations. The proposed FST is derived from unlabeled images of a subject that was scanned with both the source and the target scan protocol. After an affine registration, these images give a mapping between source and target voxels in the feature space. This mapping is then used to map all training samples to the feature representation of the test samples.

We evaluated the benefit of the proposed FST on hippocampus segmentation. Experiments were performed on two datasets: one with relatively small differences between training and test images and one with large differences. In both cases, the FST significantly improved the performance compared to using only image normalization. Additionally, we showed that our FST can be used to improve the performance of a state-of-the-art patch-based-atlas-fusion technique in case of large differences between scanners.

•

We present a feature-space transformation for image segmentation across scanners.

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This FST is trained on unlabeled images of subjects scanned with multiple scanners.

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These are used to transform training samples to values observed in target samples.

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The FST makes SVM hippocampus segmentation across scanners significantly better.

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Our FST can also increase performance of patch-based fusion methods.

An efficient and accurate method for robust inter-dataset brain extraction and comparisons with 9 other methods

Brain extraction is an important first step in many magnetic resonance neuroimaging studies. Due to variability in brain morphology and in the appearance of the brain due to differences in scanner acquisition parameters, the development of a generally applicable brain extraction algorithm has proven challenging. Learning-based brain extraction algorithms in particular perform well when the target and training images are sufficiently similar, but often perform worse when this condition is not met. In this study, we propose a new patch-based multi-atlas segmentation method for brain extraction which is specifically developed for accurate and robust processing across datasets. Using a diverse collection of labeled images from 5 different datasets, extensive comparisons were made with 9 other commonly used brain extraction methods, both before and after applying error correction (a machine learning method for automatically correcting segmentation errors) to each method. The proposed method performed equal to or better than the other methods in each of two segmentation scenarios: a challenging inter-dataset segmentation scenario in which no dataset-specific atlases were used (mean Dice coefficient 98.57%, volumetric correlation 0.994 across datasets following error correction), and an intra-dataset segmentation scenario in which only dataset-specific atlases were used (mean Dice coefficient 99.02%, volumetric correlation 0.998 across datasets following error correction). Furthermore, combined with error correction, the proposed method runs in less than one-tenth of the time required by the other top-performing methods in the challenging inter-dataset comparisons. Validation on an independent multi-centre dataset also confirmed the excellent performance of the proposed method.

The EADC-ADNI harmonized protocol for hippocampal segmentation: A validation study

Recently, a group of major international experts have completed a comprehensive effort to efficiently define a harmonized protocol for manual hippocampal segmentation that is optimized for Alzheimer's research (known as the EADC-ADNI Harmonized Protocol (the HarP)). This study compares the HarP with one of the widely used hippocampal segmentation protocols (Pruessner, 2000), based on a single automatic segmentation method trained separately with libraries made from each manual segmentation protocol. The automatic segmentation conformity with the corresponding manual segmentation and the ability to capture Alzheimer's disease related hippocampal atrophy on large datasets are measured to compare the manual protocols. In addition to the possibility of harmonizing different procedures of hippocampal segmentation, our results show that using the HarP, the automatic segmentation conformity with manual segmentation is also preserved (Dice's κ=0.88,κ=0.87 for Pruessner and HarP respectively (p = 0.726 for common training library)). Furthermore, the results show that the HarP can capture the Alzheimer's disease related hippocampal volume differences in large datasets. The HarP-derived segmentation shows large effect size (Cohen's d = 1.5883) in separating Alzheimer's Disease patients versus normal controls (AD:NC) and medium effect size (Cohen's d = 0.5747) in separating stable versus progressive Mild Cognitively Impaired patients (sMCI:pMCI). Furthermore, the area under the ROC curve for a LDA classifier trained based on age, sex and HarP-derived hippocampal volume is 0.8858 for AD:NC, and for 0.6677 sMCI:pMCI. These results show that the harmonized protocol-derived labels can be widely used in clinic and research, as a sensitive and accurate way of delineating the hippocampus.

Fast anatomy segmentation by combining coarse scale multi-atlas label fusion with fine scale corrective learning

Deformable registration based multi-atlas segmentation has been successfully applied in a broad range of anatomy segmentation applications. However, the excellent performance comes with a high computational burden due to the requirement for deformable image registration and voxel-wise label fusion. To address this problem, we investigate the role of corrective learning (Wang et al., 2011) in speeding up multi-atlas segmentation. We propose to combine multi-atlas segmentation with corrective learning in a multi-scale analysis fashion for faster speeds. First, multi-atlas segmentation is applied in a low spatial resolution. After resampling the segmentation result back to the native image space, learning-based error correction is applied to correct systematic errors due to performing multi-atlas segmentation in a low spatial resolution. In cardiac CT and brain MR segmentation experiments, we show that applying multi-atlas segmentation in a coarse scale followed by learning-based error correction in the native space can substantially reduce the overall computational cost, with only modest or no sacrificing segmentation accuracy.

Protracted hippocampal development is associated with age-related improvements in memory during early childhood

The hippocampus is a structure that is critical for memory. Previous studies have shown that age-related differences in specialization along the longitudinal axis of this structure (i.e., subregions) and within its internal circuitry (i.e., subfields) relate to age-related improvements in memory in school-age children and adults. However, the influence of age on hippocampal development and its relations with memory ability earlier in life remains under-investigated. This study examined effects of age and sex on hippocampal subregion (i.e., head, body, tail) and subfield (i.e., subiculum, CA1, CA2-4/DG) volumes, and their relations with memory, using a large sample of 4- to 8-year-old children. Results examining hippocampal subregions suggest influences of both age and sex on the hippocampal head during early childhood. Results examining subfields within hippocampal head suggest these age effects may arise from CA1, whereas sex differences may arise from subiculum and CA2-4/DG. Memory ability was not associated with hippocampal subregion volume but was associated with subfield volume. Specifically, within the hippocampal head, relations between memory and CA1 were moderated by age; in younger children bigger was better, whereas in older children smaller was superior. Within the hippocampal body, smaller CA1 and larger CA2-4/DG contributed to better memory performance across all ages. Together, these results shed light on hippocampal development during early childhood and support claims that the prolonged developmental trajectory of the hippocampus contributes to memory development early in life.