Altered structural covariance of hippocampal subregions in patients with Alzheimer's disease

Machine learning classifiers and associations of cognitive performance with hippocampal subfields in amnestic mild cognitive impairment

Background

Neuroimaging studies have demonstrated alterations in hippocampal volume and hippocampal subfields among individuals with amnestic mild cognitive impairment (aMCI). However, research on using hippocampal subfield volume modeling to differentiate aMCI from normal controls (NCs) is limited, and the relationship between hippocampal volume and overall cognitive scores remains unclear.

Methods

We enrolled 50 subjects with aMCI and 44 NCs for this study. Initially, a univariate general linear model was employed to analyze differences in the volumes of hippocampal subfields. Subsequently, two sets of dimensionality reduction methods and four machine learning techniques were applied to distinguish aMCI from NCs based on hippocampal subfield volumes. Finally, we assessed the correlation between the relative volumes of hippocampal subfields and cognitive test variables (Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA)).

Results

Significant volume differences were observed in several hippocampal subfields, notably in the left hippocampus. Specifically, the volumes of the hippocampal tail, subiculum, CA1, presubiculum, molecular layer, GC-ML-DG, CA3, CA4, and fimbria differed significantly between the two groups. The highest area under the curve (AUC) values for left and right hippocampal machine learning classifiers were 0.678 and 0.701, respectively. Moreover, the volumes of the left subiculum, left molecular layer, right subiculum, right CA1, right molecular layer, right GC-ML-DG, and right CA4 exhibited the strongest and most consistent correlations with MoCA scores.

Conclusion

Hippocampal subfield volume may serve as a predictive marker for aMCI. These findings underscore the sensitivity of hippocampal subfield volume to overall cognitive performance.

GluCEST Imaging and Structural Alterations of the Bilateral Hippocampus in First-Episode and Early-Onset Major Depression Disorder

BACKGROUND

Glutamate dysregulation is one of the key pathogenic mechanisms of major depressive disorder (MDD), and glutamate chemical exchange saturation transfer (GluCEST) has been used for glutamate measurement in some brain diseases but rarely in depression.

PURPOSE

To investigate the GluCEST changes in hippocampus in MDD and the relationship between glutamate and hippocampal subregional volumes.

STUDY TYPE

Cross-sectional.

SUBJECTS

Thirty-two MDD patients (34% males; 22.03 ± 7.21 years) and 47 healthy controls (HCs) (43% males; 22.00 ± 3.28 years).

FIELD STRENGTH/SEQUENCE

3.0 T; magnetization prepared rapid gradient echo (MPRAGE) for three-dimensional T1-weighted images, two-dimensional turbo spin echo GluCEST, and multivoxel chemical shift imaging (CSI) for proton magnetic resonance spectroscopy (1 H MRS).

ASSESSMENT

GluCEST data were quantified by magnetization transfer ratio asymmetry (MTRasym ) analysis and assessed by the relative concentration of 1 H MRS-measured glutamate. FreeSurfer was used for hippocampus segmentation.

STATISTICAL TESTS

The independent sample t test, Mann-Whitney U test, Spearman's correlation, and partial correlation analysis were used. P < 0.05 was considered statistically significant.

RESULTS

In the left hippocampus, GluCEST values were significantly decreased in MDD (2.00 ± 1.08 [MDD] vs. 2.62 ± 1.41 [HCs]) and showed a significantly positive correlation with Glx/Cr (r = 0.37). GluCEST values were significantly positively correlated with the volumes of CA1 (r = 0.40), subiculum (r = 0.40) in the left hippocampus and CA1 (r = 0.51), molecular_layer_HP (r = 0.50), GC-ML-DG (r = 0.42), CA3 (r = 0.44), CA4 (r = 0.44), hippocampus-amygdala-transition-area (r = 0.46), and the whole hippocampus (r = 0.47) in the right hippocampus. Hamilton Depression Rating Scale scores showed significantly negative correlations with the volumes of the left presubiculum (r = -0.40), left parasubiculum (r = -0.47), and right presubiculum (r = -0.41).

DATA CONCLUSION

GluCEST can be used to measure glutamate changes and help to understand the mechanism of hippocampal volume loss in MDD. Hippocampal volume changes are associated with disease severity.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Reorganization of the structural connectome during vision recovery in pituitary adenoma patients post-transsphenoidal surgery

Pituitary adenomas (PAs) can exert pressure on the optic apparatus, leading to visual impairment. A subset of patients may observe a swift improvement in their vision following surgery. Nevertheless, the alterations in the structural connectome during the early postoperative period remain largely unexplored. The research employed probabilistic tractography, graph theoretical analysis, and statistical methods on preoperative and postoperative structural magnetic resonance imaging and diffusion tensor images from 13 PA patients. Postoperative analysis revealed an increase in global and local efficiency, signifying improved network capacity for parallel information transfer and fault tolerance, respectively. Enhanced clustering coefficient and reduced shortest path length were also observed, suggesting a more regular network organization and shortened communication steps within the brain network. Furthermore, alterations in node graphical properties were detected, implying a restructuring of the network's control points, possibly contributing to more efficient visual processing. These findings propose that rapid vision recovery post-surgery may be associated with significant reorganization of the brain's structural connectome, enhancing the efficiency and adaptability of the network, thereby facilitating improved visual processing.

Degeneracy and disordered brain networks in psychiatric patients using multivariate structural covariance analyzes

Introduction

In this study, we applied multivariate methods to identify brain regions that have a critical role in shaping the connectivity patterns of networks associated with major psychiatric diagnoses, including schizophrenia (SCH), major depressive disorder (MDD) and bipolar disorder (BD) and healthy controls (HC). We used T1w images from 164 subjects: Schizophrenia (n = 17), bipolar disorder (n = 25), major depressive disorder (n = 68) and a healthy control group (n = 54).

Methods

We extracted regions of interest (ROIs) using a method based on the SHOOT algorithm of the SPM12 toolbox. We then performed multivariate structural covariance between the groups. For the regions identified as significant in t term of their covariance value, we calculated their eigencentrality as a measure of the influence of brain regions within the network. We applied a significance threshold of p = 0.001. Finally, we performed a cluster analysis to determine groups of regions that had similar eigencentrality profiles in different pairwise comparison networks in the observed groups.

Results

As a result, we obtained 4 clusters with different brain regions that were diagnosis-specific. Cluster 1 showed the strongest discriminative values between SCH and HC and SCH and BD. Cluster 2 had the strongest discriminative value for the MDD patients, cluster 3 - for the BD patients. Cluster 4 seemed to contribute almost equally to the discrimination between the four groups.

Discussion

Our results suggest that we can use the multivariate structural covariance method to identify specific regions that have higher predictive value for specific psychiatric diagnoses. In our research, we have identified brain signatures that suggest that degeneracy shapes brain networks in different ways both within and across major psychiatric disorders.

Spatial navigation is associated with subcortical alterations and progression risk in subjective cognitive decline

BACKGROUND

Subjective cognitive decline (SCD) may serve as a symptomatic indicator for preclinical Alzheimer's disease; however, SCD is a heterogeneous entity regarding clinical progression. We aimed to investigate whether spatial navigation could reveal subcortical structural alterations and the risk of progression to objective cognitive impairment in SCD individuals.

METHODS

One hundred and eighty participants were enrolled: those with SCD (n = 80), normal controls (NCs, n = 77), and mild cognitive impairment (MCI, n = 23). SCD participants were further divided into the SCD-good (G-SCD, n = 40) group and the SCD-bad (B-SCD, n = 40) group according to their spatial navigation performance. Volumes of subcortical structures were calculated and compared among the four groups, including basal forebrain, thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens. Topological properties of the subcortical structural covariance network were also calculated. With an interval of 1.5 years ± 12 months of follow-up, the progression rate to MCI was compared between the G-SCD and B-SCD groups.

RESULTS

Volumes of the basal forebrain, the right hippocampus, and their respective subfields differed significantly among the four groups (p < 0.05, false discovery rate corrected). The B-SCD group showed lower volumes in the basal forebrain than the G-SCD group, especially in the Ch4p and Ch4a-i subfields. Furthermore, the structural covariance network of the basal forebrain and right hippocampal subfields showed that the B-SCD group had a larger Lambda than the G-SCD group, which suggested weakened network integration in the B-SCD group. At follow-up, the B-SCD group had a significantly higher conversion rate to MCI than the G-SCD group.

CONCLUSION

Compared to SCD participants with good spatial navigation performance, SCD participants with bad performance showed lower volumes in the basal forebrain, a reorganized structural covariance network of subcortical nuclei, and an increased risk of progression to MCI. Our findings indicated that spatial navigation may have great potential to identify SCD subjects at higher risk of clinical progression, which may contribute to making more precise clinical decisions for SCD individuals who seek medical help.

Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review

Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.

Hypertension Status Moderated the Relationship between the Hippocampal Subregion of the Left GC-ML-DG and Cognitive Performance in Subjective Cognitive Decline

Background. To investigate the relationship between hypertension status, hippocampus/hippocampal subregion structural alteration, and cognitive performance in subjective cognitive decline (SCD). Methods. All participants were divided into two groups according to blood pressure status: SCD without hypertension and SCD with hypertension. The cognitive assessments and T1-MPRAGE brain MRI were performed to measure the cognitive function and the volume of the hippocampus and hippocampal subregions. Association and mediating/moderating effects were analyzed between the volume of hippocampus/hippocampal subregions and cognitive scores. Results. Compared to the SCD without hypertension, we found (1) increased reaction time (RT) of the Go/No go test, compatible test, and divided attention visual task and (2) decreased volume of the left whole hippocampal/left subiculum/left CA1/left presubiculum/left parasubiculum/left molecular layer HP/left GC-ML-DG/left HATA in SCD with hypertension. There was a significant negative association between the volume of the left GC-ML-DG and Go/No go test RT in SCD without hypertension. A significant moderating effect of hypertension status on the relationship between the volume of the left GC-ML-DG and Go/No go test RT was found. Conclusion. The results suggested that hypertension status affects inhibitory control function and visual divided attention which may be related to the reduction of hippocampus/hippocampal subregion volume in SCD. Limitations. The study has several limitations. First, this study does not include a healthy control group. In further studies, healthy controls may need to assess the interaction between hypertension status and disease status on cognitive function. Second, we defined the hypertension status using with or without hypertension disease. More detailed parameters of hypertension status need to be further studied. Third, our study was a small number of participants/single-center and cross-sectional study, which may hinder its generalization. A large-sample/multicenter, longitudinal study is helpful to comprehensively understand the relationship between hypertension status and cognitive function in SCD patients.