A controlled comparison of thickness, volume and surface areas from multiple cortical parcellation packages

Metric magnetic resonance imaging analysis reveals pronounced substantia-innominata atrophy in dementia with Lewy bodies with a psychiatric onset

Background

Dementia with Lewy bodies (DLB) is a type of dementia often diagnosed in older patients. Since its initial symptoms range from delirium to psychiatric and cognitive symptoms, the diagnosis is often delayed.

Objectives

In our study, we evaluated the magnetic resonance imaging (MRI) of patients suffering from DLB in correlation with their initial symptoms taking a new pragmatic approach entailing manual measurements in addition to an automated volumetric analysis of MRI.

Methods

A total of 63 patients with diagnosed DLB and valid 3D data sets were retrospectively and blinded evaluated. We assessed atrophy patterns (1) manually for the substantia innominata and (2) via FastSurfer for the most common supratentorial regions. Initial symptoms were categorized by (1) mild cognitive impairment (MCI), (2) psychiatric episodes, and (3) delirium.

Results

Manual metric MRI measurements revealed moderate, but significant substantia-innominata (SI) atrophy in patients with a psychiatric onset. FastSurfer analysis revealed no regional volumetric differences between groups.

Conclusion

The SI in patients with DLB and a psychiatric-onset is more atrophied than that in patients with initial MCI. Our results suggest potential differences in SI between DLB subtypes at the prodromal stage, which are useful when taking a differential-diagnostic approach. This finding should be confirmed in larger patient cohorts.

A multi-layer soft lattice based model for Chinese clinical named entity recognition

Objective

Named entity recognition (NER) is a key and fundamental part of many medical and clinical tasks, including the establishment of a medical knowledge graph, decision-making support, and question answering systems. When extracting entities from electronic health records (EHRs), NER models mostly apply long short-term memory (LSTM) and have surprising performance in clinical NER. However, increasing the depth of the network is often required by these LSTM-based models to capture long-distance dependencies. Therefore, these LSTM-based models that have achieved high accuracy generally require long training times and extensive training data, which has obstructed the adoption of LSTM-based models in clinical scenarios with limited training time.

Method

Inspired by Transformer, we combine Transformer with Soft Term Position Lattice to form soft lattice structure Transformer, which models long-distance dependencies similarly to LSTM. Our model consists of four components: the WordPiece module, the BERT module, the soft lattice structure Transformer module, and the CRF module.

Result

Our experiments demonstrated that this approach increased the F1 by 1–5% in the CCKS NER task compared to other models based on LSTM with CRF and consumed less training time. Additional evaluations showed that lattice structure transformer shows good performance for recognizing long medical terms, abbreviations, and numbers. The proposed model achieve 91.6% f-measure in recognizing long medical terms and 90.36% f-measure in abbreviations, and numbers.

Conclusions

By using soft lattice structure Transformer, the method proposed in this paper captured Chinese words to lattice information, making our model suitable for Chinese clinical medical records. Transformers with Mutilayer soft lattice Chinese word construction can capture potential interactions between Chinese characters and words.

Changes in Volume of Subregions Within Basal Ganglia in Obsessive–Compulsive Disorder: A Study With Atlas-Based and VBM Methods

Background

The role of basal ganglia in the pathogenesis of obsessive–compulsive disorder (OCD) remains unclear. The studies on volume changes of basal ganglia in OCD commonly use the VBM method; however, the Atlas-based method used in such research has not been reported. Atlas-based method has a lower false positive rate compared with VBM method, thus having advantages partly.

Objectives

The current study aimed to detect the volume changes of subregions within basal ganglia in OCD using Atlas-based method to further delineate the precise neural circuitry of OCD. What is more, we explored the influence of software used in Atlas-based method on the volumetric analysis of basal ganglia and compared the results of Atlas-based method and regularly used VBM method.

Methods

We analyzed the brain structure images of 37 patients with OCD and 41 healthy controls (HCs) using the VBM method, Atlas-based method based on SPM software, or Freesurfer software to find the areas with significant volumetric variation between the two groups, and calculated the effects size of these areas.

Results

VBM analysis revealed a significantly increased volume of bilateral lenticular nucleus in patients compared to HCs. In contrast, Atlas-based method based on Freesurfer revealed significantly increased volume of left globus pallidus in patients, and the largest effect size of volumetric variation was revealed by Freesurfer analysis.

Conclusions

This study showed that the volume of bilateral lenticular nucleus significantly increased in patients compared to HCs, especially left globus pallidus, which was in accordance with the previous findings. In addition, Freesurfer is better than SPM and a good choice for Atlas-based volumetric analysis of basal ganglia.

Manual and automated analysis of atrophy patterns in dementia with Lewy bodies on MRI

Background

Dementia with Lewy bodies (DLB) is the second most common dementia type in patients older than 65 years. Its atrophy patterns remain unknown. Its similarities to Parkinson's disease and differences from Alzheimer's disease are subjects of current research.

Methods

The aim of our study was (i) to form a group of patients with DLB (and a control group) and create a 3D MRI data set (ii) to volumetrically analyze the entire brain in these groups, (iii) to evaluate visual and manual metric measurements of the innominate substance for real-time diagnosis, and (iv) to compare our groups and results with the latest literature. We identified 102 patients with diagnosed DLB in our psychiatric and neurophysiological archives. After exclusion, 63 patients with valid 3D data sets remained. We compared them with a control group of 25 patients of equal age and sex distribution. We evaluated the atrophy patterns in both (1) manually and (2) via Fast Surfers segmentation and volumetric calculations. Subgroup analyses were done of the CSF data and quality of 3D T1 data sets.

Results

Concordant with the literature, we detected moderate, symmetric atrophy of the hippocampus, entorhinal cortex and amygdala, as well as asymmetric atrophy of the right parahippocampal gyrus in DLB. The caudate nucleus was unaffected in patients with DLB, while all the other measured territories were slightly too moderately atrophied. The area under the curve analysis of the left hippocampus volume ratio (< 3646mm³) revealed optimal 76% sensitivity and 100% specificity (followed by the right hippocampus and left amygdala). The substantia innominata’s visual score attained a 51% optimal sensitivity and 84% specificity, and the measured distance 51% optimal sensitivity and 68% specificity in differentiating DLB from our control group.

Conclusions

In contrast to other studies, we observed a caudate nucleus sparing atrophy of the whole brain in patients with DLB. As the caudate nucleus is known to be the last survivor in dopamine-uptake, this could be the result of an overstimulation or compensation mechanism deserving further investigation. Its relative hypertrophy compared to all other brain regions could enable an imaging based identification of patients with DLB via automated segmentation and combined volumetric analysis of the hippocampus and amygdala.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12883-022-02642-0.

Comparison of cortical and subcortical structural segmentation methods in Alzheimer's disease: A statistical approach

BACKGROUND

Automated segmentation methods are developed to help with the segmentation of different brain areas. However, their reliability has yet to be fully investigated. To have a more comprehensive understanding of the distribution of changes in Alzheimer's disease (AD), as well as investigating the reliability of different segmentation methods, in this study we compared volumes of cortical and subcortical brain segments, using HIPS, volBrain, CAT and BrainSuite automated segmentation methods between AD, mild cognitive impairment (MCI) and healthy controls (HC).

METHODS

A total of 182 MRI images were taken from the minimal interval resonance imaging in Alzheimer's disease (MIRIAD; 22 AD and 22 HC) and the Alzheimer's disease neuroimaging initiative database (ADNI; 43 AD, 50 MCI and 45 HC) datasets. Statistical methods were used to compare different groups as well as the correlation between different methods.

RESULTS

The two methods of volBrain and CAT showed a strong correlation (p's < 0.035 Bonferroni corrected for multiple comparisons). The two methods, however, showed no significant correlation with BrainSuite (p's > 0.820 Bonferroni corrected). Furthermore, BrainSuite did not follow the same trend as the other three methods and only HIPS, volBrain and CAT showed strong conformity with the past literature with strong correlation with mini mental state examination (MMSE) scores.

CONCLUSION

Our results showed that automated segmentation methods HIPS, volBrain and CAT can be used in the classification of HC, AD and MCI. This is an indication that such methods can be used to inform researchers and clinicians of underlying mechanisms and progression of AD.

Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation

There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.

Diagnosis of early mild cognitive impairment using a multiobjective optimization algorithm based on T1-MRI data

Alzheimer's disease (AD) is the most prevalent form of dementia. The accurate diagnosis of AD, especially in the early phases is very important for timely intervention. It has been suggested that brain atrophy, as measured with structural magnetic resonance imaging (sMRI), can be an efficacy marker of neurodegeneration. While classification methods have been successful in diagnosis of AD, the performance of such methods have been very poor in diagnosis of those in early stages of mild cognitive impairment (EMCI). Therefore, in this study we investigated whether optimisation based on evolutionary algorithms (EA) can be an effective tool in diagnosis of EMCI as compared to cognitively normal participants (CNs). Structural MRI data for patients with EMCI (n = 54) and CN participants (n = 56) was extracted from Alzheimer's disease Neuroimaging Initiative (ADNI). Using three automatic brain segmentation methods, we extracted volumetric parameters as input to the optimisation algorithms. Our method achieved classification accuracy of greater than 93%. This accuracy level is higher than the previously suggested methods of classification of CN and EMCI using a single- or multiple modalities of imaging data. Our results show that with an effective optimisation method, a single modality of biomarkers can be enough to achieve a high classification accuracy.

Basal ganglia correlates of wellbeing in early adolescence

It has been suggested that biological markers that define mental health are different to those that define mental illness. The basal ganglia changes dramatically over adolescence and has been linked to wellbeing and mental health disorders in young people. However, there remains a paucity of research on wellbeing and brain structure in early adolescence. This cross-sectional study examined relationships between grey matter volume (GMV) of basal ganglia regions (caudate, putamen, pallidum and nucleus accumbens) and self-reported wellbeing (COMPAS-W), in a sample of Australian adolescents aged 12 years (N = 49, M = 12.6, 46.9% female). Significant negative associations were found between left hemisphere caudate GMV and scores on 'total wellbeing', 'composure' and 'positivity'. The results of this study indicate that smaller caudate GMV at age 12 is linked to increased subjective wellbeing. While seemingly counter-intuitive, our finding is consistent with previous research of decreased GMV in the pons and increased COMPAS-W scores in adults. Our results suggest that protective neurobiological factors may be identifiable early in adolescence and be linked to specific types of wellbeing (such as positive affect and optimism). This has implications for interventions targeted at building resilience against mental health disorders in young people.

Neuroimaging and DNA Methylation: An Innovative Approach to Study the Effects of Early Life Stress on Developmental Plasticity

DNA methylation plays a key role in neural cell fate and provides a molecular link between early life stress and later-life behavioral phenotypes. Here, studies that combine neuroimaging methods and DNA methylation analysis in pediatric population with a history of adverse experiences were systematically reviewed focusing on: targeted genes and neural correlates; statistical models used to examine the link between DNA methylation and neuroimaging data also considering early life stress and behavioral outcomes. We identified 8 studies that report associations between DNA methylation and brain structure/functions in infants, school age children and adolescents faced with early life stress condition (e.g., preterm birth, childhood maltreatment, low socioeconomic status, and less-than optimal caregiving). Results showed that several genes were investigated (e.g., OXTR, SLC6A4, FKBP5, and BDNF) and different neuroimaging techniques were performed (MRI and f-NIRS). Statistical model used ranged from correlational to more complex moderated mediation models. Most of the studies (n = 5) considered DNA methylation and neural correlates as mediators in the relationship between early life stress and behavioral phenotypes. Understanding what role DNA methylation and neural correlates play in interaction with early life stress and behavioral outcomes is crucial to promote theory-driven studies as the future direction of this research fields.

Review article: Structural brain alterations in prelingually deaf

Functional studies show that our brain has a remarkable ability to reorganize itself in the absence of one or more sensory modalities. In this review, we gathered all the available articles investigating structural alterations in congenitally deaf subjects. Some concentrated only on specific regions of interest (e.g., auditory areas), while others examined the whole brain. The majority of structural alterations were observed in the auditory white matter and were more pronounced in the right hemisphere. A decreased white matter volume or fractional anisotropy in the auditory areas were the most common findings in congenitally deaf subjects. Only a few studies observed alterations in the auditory grey matter. Preservation of the grey matter might be due to the cross-modal plasticity as well as due to the lack of sensitivity of methods used for microstructural alterations of grey matter. Structural alterations were also observed in the frontal, visual, and other cerebral regions as well as in the cerebellum. The observed structural brain alterations in the deaf can probably be attributed mainly to the cross-modal plasticity in the absence of sound input and use of sign instead of spoken language.

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel-wise contributions to the prediction in a single image, resulting in "explanation maps" that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population-based, rather than a subject-specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel-based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.