Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer's Disease

T1-Weighted Imaging-Based Hippocampal Radiomics in the Diagnosis of Alzheimer's Disease

RATIONALE AND OBJECTIVES

To investigate the potential of T1-weighted imaging (T1WI)-based hippocampal radiomics as imaging markers for the diagnosis of Alzheimer's disease (AD) and their efficacy in discriminating between mild cognitive impairment (MCI) and dementia in AD.

METHODS

A total of 126 AD patients underwent T1WI-based magnetic resonance imaging (MRI) examinations, along with 108 age-sex-matched healthy controls (HC). This was a retrospective, single-center study conducted from November 2021 to February 2023. AD patients were categorized into two groups based on disease progression and cognitive function: AD-MCI and dementia (AD-D). T1WI-based radiomics features of the bilateral hippocampi were extracted. To diagnose AD and differentiate between AD-MCI and AD-D, predictive models were developed using random forest (RF), logistic regression (LR), and support vector machine (SVM). We compared radiomics features between the AD and HC groups, as well as within the subgroups of AD-MCI and AD-D. Area under the curve (AUC), accuracy, sensitivity, and specificity were all used to assess model performance. Furthermore, correlations between radiomics features and Mini-Mental State Examination (MMSE) scores, tau protein phosphorylated at threonine 181 (P-tau-181), and amyloid β peptide1-42 (Aβ1-42) were analyzed.

RESULTS

The RF model demonstrated superior performance in distinguishing AD from HC (AUC=0.961, accuracy=90.8%, sensitivity=90.7%, specificity=90.9%) and in identifying AD-MCI and AD-D (AUC=0.875, accuracy=80.7%, sensitivity=87.2%, specificity=73.2%) compared to the other models. Additionally, radiomics features were correlated with MMSE scores, P-tau-181, and Aβ1-42 levels in AD.

CONCLUSION

T1WI-based hippocampal radiomics features are valuable for diagnosing AD and identifying AD-MCI and AD-D.

Geodesic shape regression based deep learning segmentation for assessing longitudinal hippocampal atrophy in dementia progression

Longitudinal hippocampal atrophy is commonly used as progressive marker assisting clinical diagnose of dementia. However, precise quantification of the atrophy is limited by longitudinal segmentation errors resulting from MRI artifacts across multiple independent scans. To accurately segment the hippocampal morphology from longitudinal 3T T1-weighted MR images, we propose a diffeomorphic geodesic guided deep learning method called the GeoLongSeg to mitigate the longitudinal variabilities that unrelated to diseases by enhancing intra-individual morphological consistency. Specifically, we integrate geodesic shape regression, an evolutional model that estimates smooth deformation process of anatomical shapes, into a two-stage segmentation network. We adopt a 3D U-Net in the first-stage network with an enhanced attention mechanism for independent segmentation. Then, a hippocampal shape evolutional trajectory is estimated by geodesic shape regression and fed into the second network to refine the independent segmentation. We verify that GeoLongSeg outperforms other four state-of-the-art segmentation pipelines in longitudinal morphological consistency evaluated by test-retest reliability, variance ratio and atrophy trajectories. When assessing hippocampal atrophy in longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), results based on GeoLongSeg exhibit spatial and temporal local atrophy in bilateral hippocampi of dementia patients. These features derived from GeoLongSeg segmentation exhibit the greatest discriminatory capability compared to the outcomes of other methods in distinguishing between patients and normal controls. Overall, GeoLongSeg provides an accurate and efficient segmentation network for extracting hippocampal morphology from longitudinal MR images, which assist precise atrophy measurement of the hippocampus in early stage of dementia.

Predicting mild cognitive impairment in older adults: A machine learning analysis of the Alzheimer's Disease Neuroimaging Initiative

AIM

Mild cognitive impairment (MCI) in older adults is potentially devastating, but an accurate prediction model is still lacking. We hypothesized that neuropsychological tests and MRI-related markers could predict the onset of MCI early.

METHODS

We analyzed data from 306 older adults who were cognitive normal (CN) attending the Alzheimer's Disease Neuroimaging Initiative sequentially (474 pairs of visits) within 3 years. There were 231 pairs of MCI conversion (CN to MCI), and 242 pairs of CN maintenance (CN to CN). Variables on demographic, neuropsychological tests, genetic, and MRI-related markers were collected. Machine learning was used to construct MCI prediction models, comparing the area under the receiver operating characteristic curve (AUC) as the primary metric of performance. Important predictors were ranked for the optimal model.

RESULTS

The baseline age of the study sample was 74.8 years old. The best-performing model (gradient boosting decision tree) with 13 variables predicted MCI with an AUC of 0.819, and the rank of variable importance showed that intracranial volume, hippocampal volume, and score from task 4 (word recognition) of the Alzheimer's Disease Assessment Scale were important predictors of MCI.

CONCLUSIONS

With the help of machine learning, fewer neuropsychological tests and MRI-related markers are required to accurately predict MCI within 3 years, thereby facilitating targeted intervention. Geriatr Gerontol Int 2024; 24: 96-101.

Atrophy asymmetry in hippocampal subfields in patients with Alzheimer's disease and mild cognitive impairment

Volumetric analysis of hippocampal subfields and their asymmetry assessment recently has been useful biomarkers in neuroscience. In this study, hippocampal subfields atrophy and pattern of their asymmetry in the patient with Alzheimer's disease (AD) and mild cognitive impairment (MCI) were evaluated. MRI images of 20 AD patients, 20 MCI patients, and 20 healthy control (HC) were selected. The volumes of hippocampal subfields were extracted automatically using Freesurfer toolkit. The subfields asymmetry index (AI) and laterality ([Formula: see text]) were also evaluated. Analysis of covariance was used to compare the subfields volume between three patient groups (age and gender as covariates). We used ANOVA (P < 0.05) test for multiple comparisons with Bonferroni's post hoc correction method. Hippocampal subfields volume in AD patients were significantly lower than HC and MCI groups (P < 0.02); however, no significant difference was observed between MCI and HC groups. The asymmetry index (AI) in some subfields was significantly different between AD and MCI, as well as between AD and HC, while there was not any significant difference between MCI groups with HC. In all three patient groups, rightward laterality ([Formula: see text]) was seen in several subfields except subiculum, presubiculum, and parasubiculum, while in AD patient, rightward lateralization slightly decrease. Hippocampal subfields asymmetry can be used as a quantitative biomarker in neurocognitive disorders. In this study, it was observed that the asymmetry index of some subfields in AD is significantly different from MCI. In AD, patient rightward laterality was less MCI an HC group.

A multi-expert ensemble system for predicting Alzheimer transition using clinical features

Alzheimer’s disease (AD) diagnosis often requires invasive examinations (e.g., liquor analyses), expensive tools (e.g., brain imaging) and highly specialized personnel. The diagnosis commonly is established when the disorder has already caused severe brain damage, and the clinical signs begin to be apparent. Instead, accessible and low-cost approaches for early identification of subjects at high risk for developing AD years before they show overt symptoms are fundamental to provide a critical time window for more effective clinical management, treatment, and care planning. This article proposes an ensemble-based machine learning algorithm for predicting AD development within 9 years from first overt signs and using just five clinical features that are easily detectable with neuropsychological tests. The validation of the system involved both healthy individuals and mild cognitive impairment (MCI) patients drawn from the ADNI open dataset, at variance with previous studies that considered only MCI. The system shows higher levels of balanced accuracy, negative predictive value, and specificity than other similar solutions. These results represent a further important step to build a preventive fast-screening machine-learning-based tool to be used as a part of routine healthcare screenings.

Alzheimer's Disease Diagnosis and Biomarker Analysis Using Resting-State Functional MRI Functional Brain Network With Multi-Measures Features and Hippocampal Subfield and Amygdala Volume of Structural MRI

Accurate diagnosis of the initial phase of Alzheimer's disease (AD) is essential and crucial. The objective of this research was to employ efficient biomarkers for the diagnostic analysis and classification of AD based on combining structural MRI (sMRI) and resting-state functional MRI (rs-fMRI). So far, several anatomical MRI imaging markers for AD diagnosis have been identified. The use of cortical and subcortical volumes, the hippocampus, and amygdala volume, as well as genetic patterns, has proven to be beneficial in distinguishing patients with AD from the healthy population. The fMRI time series data have the potential for specific numerical information as well as dynamic temporal information. Voxel and graphical analyses have gained popularity for analyzing neurodegenerative diseases, such as Alzheimer's and its prodromal phase, mild cognitive impairment (MCI). So far, these approaches have been utilized separately for the diagnosis of AD. In recent studies, the classification of cases of MCI into those that are not converted for a certain period as stable MCI (MCIs) and those that converted to AD as MCIc has been less commonly reported with inconsistent results. In this study, we verified and validated the potency of a proposed diagnostic framework to identify AD and differentiate MCIs from MCIc by utilizing the efficient biomarkers obtained from sMRI, along with functional brain networks of the frequency range .01-.027 at the resting state and the voxel-based features. The latter mainly included default mode networks (amplitude of low-frequency fluctuation [ALFF], fractional ALFF [ALFF], and regional homogeneity [ReHo]), degree centrality (DC), and salience networks (SN). Pearson's correlation coefficient for measuring fMRI functional networks has proven to be an efficient means for disease diagnosis. We applied the graph theory to calculate nodal features (nodal degree [ND], nodal path length [NL], and between centrality [BC]) as a graphical feature and analyzed the connectivity link between different brain regions. We extracted three-dimensional (3D) patterns to calculate regional coherence and then implement a univariate statistical t-test to access a 3D mask that preserves voxels showing significant changes. Similarly, from sMRI, we calculated the hippocampal subfield and amygdala nuclei volume using Freesurfer (version 6). Finally, we implemented and compared the different feature selection algorithms to integrate the structural features, brain networks, and voxel features to optimize the diagnostic identifications of AD using support vector machine (SVM) classifiers. We also compared the performance of SVM with Random Forest (RF) classifiers. The obtained results demonstrated the potency of our framework, wherein a combination of the hippocampal subfield, the amygdala volume, and brain networks with multiple measures of rs-fMRI could significantly enhance the accuracy of other approaches in diagnosing AD. The accuracy obtained by the proposed method was reported for binary classification. More importantly, the classification results of the less commonly reported MCIs vs. MCIc improved significantly. However, this research involved only the AD Neuroimaging Initiative (ADNI) cohort to focus on the diagnosis of AD advancement by integrating sMRI and fMRI. Hence, the study's primary disadvantage is its small sample size. In this case, the dataset we utilized did not fully reflect the whole population. As a result, we cannot guarantee that our findings will be applicable to other populations.

The Road to Personalized Medicine in Alzheimer’s Disease: The Use of Artificial Intelligence

Dementia remains an extremely prevalent syndrome among older people and represents a major cause of disability and dependency. Alzheimer’s disease (AD) accounts for the majority of dementia cases and stands as the most common neurodegenerative disease. Since age is the major risk factor for AD, the increase in lifespan not only represents a rise in the prevalence but also adds complexity to the diagnosis. Moreover, the lack of disease-modifying therapies highlights another constraint. A shift from a curative to a preventive approach is imminent and we are moving towards the application of personalized medicine where we can shape the best clinical intervention for an individual patient at a given point. This new step in medicine requires the most recent tools and analysis of enormous amounts of data where the application of artificial intelligence (AI) plays a critical role on the depiction of disease–patient dynamics, crucial in reaching early/optimal diagnosis, monitoring and intervention. Predictive models and algorithms are the key elements in this innovative field. In this review, we present an overview of relevant topics regarding the application of AI in AD, detailing the algorithms and their applications in the fields of drug discovery, and biomarkers.

In Vivo Characterization of Cortical and White Matter Microstructural Pathology in Growth Hormone-Secreting Pituitary Adenoma

Background

The growth hormone (GH) and insulin-like-growth factor 1 (IGF-1) axis has long been recognized for its critical role in brain growth, development. This study was designed to investigate microstructural pathology in the cortex and white matter in growth hormone-secreting pituitary adenoma, which characterized by excessive secretion of GH and IGF-1.

Methods

29 patients with growth hormone-secreting pituitary adenoma (acromegaly) and 31 patients with non-functional pituitary adenoma as controls were recruited and assessed using neuropsychological test, surface-based morphometry, T1/T2-weighted myelin-sensitive magnetic resonance imaging, neurite orientation dispersion and density imaging, and diffusion tensor imaging.

Results

Compared to controls, we found 1) acromegaly had significantly increased cortical thickness throughout the bilateral cortex (pFDR < 0.05). 2) T1/T2-weighted ratio in the cortex were decreased in the bilateral occipital cortex and pre/postcentral central gyri but increased in the bilateral fusiform, insular, and superior temporal gyri in acromegaly (pFDR < 0.05). 3) T1/T2-weighted ratio were decreased in most bundles, and only a few areas showed increases in acromegaly (pFDR < 0.05). 4) Neurite density index (NDI) was significantly lower throughout the cortex and bundles in acromegaly (pTFCE < 0.05). 5) lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) in extensive bundles in acromegaly (pTFCE < 0.05). 6) microstructural pathology in the cortex and white matter were associated with neuropsychological dysfunction in acromegaly.

Conclusions

Our findings suggested that long-term persistent and excess serum GH/IGF-1 levels alter the microstructure in the cortex and white matter in acromegaly, which may be responsible for neuropsychological dysfunction.

Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer's disease

BACKGROUND

Longitudinal studies using structural magnetic resonance imaging (MRI) and neuropsychological measurements (NMs) allow a noninvasive means of following the subtle anatomical changes occurring during the evolution of AD.

NEW METHOD

This paper compared two approaches for the construction of longitudinal predictive models: a) two-group comparison between converter and nonconverter MCI subjects and b) longitudinal survival analysis. Predictive models combined MRI-based markers with NMs and included demographic and clinical information as covariates. Both approaches employed linear mixed effects modeling to capture the longitudinal trajectories of the markers. The two-group comparison approaches used linear discriminant analysis and the survival analysis used risk ratios obtained from the extended Cox model and logistic regression.

RESULTS

The proposed approaches were developed and evaluated using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with a total of 1330 visits from 321 subjects. With both approaches, a very small number of features were selected. These markers are easily interpretable, generating robust, verifiable and reliable predictive models. Our best models predicted conversion with 78% accuracy at baseline (AUC = 0.860, 79% sensitivity, 76% specificity). As more visits were made, longitudinal predictive models improved their predictions with 85% accuracy (AUC = 0.944, 86% sensitivity, 85% specificity).

COMPARISON WITH EXISTING METHOD

Unlike the recently published models, there was also an improvement in the prediction accuracy of the conversion to AD when considering the longitudinal trajectory of the patients.

CONCLUSIONS

The survival-based predictive models showed a better balance between sensitivity and specificity with respect to the models based on the two-group comparison approach.

Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields

Background

Mild cognitive impairment (MCI) is subtle cognitive decline with an estimated 10-15% yearly conversion rate toward Alzheimer's disease (AD). It remains unexplored in brain cortical association areas in different lobes and its changes with progression and conversion of MCI.

Methods

Brain structural magnetic resonance (MR) images were collected from 102 stable MCI (sMCI) patients. One hundred eleven were converted MCI (cMCI) patients, and 109 were normal control (NC). The cortical surface features and volumes of subcortical hippocampal subfields were calculated using the FreeSurfer software, followed by an analysis of variance (ANOVA) model, to reveal the differences between the NC-sMCI, NC-cMCI, and sMCI-cMCI groups. Afterward, the support vector machine-recursive feature elimination (SVM-RFE) method was applied to determine the differences between the groups.

Results

The experimental results showed that there were progressive degradations in either range or degree of the brain structure from NC to sMCI, and then to cMCI. The SVM classifier obtained accuracies with 64.62%, 78.96%, and 70.33% in the sMCI-NC, cMCI-NC, and cMCI-sMCI groups, respectively, using the volumes of hippocampal subfields independently. The combination of the volumes from the hippocampal subfields and cortical measurements could significantly increase the performance to 71.86%, 84.64%, and 76.86% for the sMCI-NC, cMCI-NC, and cMCI-sMCI classifications, respectively. Also, the brain regions corresponding to the dominant features with strong discriminative power were widely located in the temporal, frontal, parietal, olfactory cortexes, and most of the hippocampal subfields, which were associated with cognitive decline, memory impairment, spatial navigation, and attention control.

Conclusions

The combination of cortical features with the volumes of hippocampal subfields could supply critical information for MCI detection and its conversion.

Structural plasticity of the bilateral hippocampus in glioma patients

This study investigates the structural plasticity and neuronal reaction of the hippocampus in glioma patient pre-surgery. Ninety-nine glioma patients without bilateral hippocampus involvement (low-grade, n=52; high-grade, n=47) and 80 healthy controls with 3D T1 images and resting-fMRI were included. Hippocampal volume and dynamic amplitude of low-frequency fluctuation (dALFF) were analyzed among groups. Relationships between hippocampal volume and clinical characteristics were assessed. We observed remote hippocampal volume increases in low- and high-grade glioma and a greater response of the ipsilateral hippocampus than the contralesional hippocampus. The bilateral hippocampal dALFF was significantly increased in high-grade glioma. Tumor-associated epilepsy and the IDH-1 mutation did not affect hippocampal volume in glioma patients. No significant relationship between hippocampal volume and age was observed in high-grade glioma. The Kaplan-Meier curve and log-rank test revealed that large hippocampal volume was associated with shorter overall survival (OS) compared with small hippocampal volume (p=0.007). Multivariate Cox regression analysis revealed that large hippocampal volume was an independent predictor of unfavorable OS (HR=3.597, 95% CI: 1.160-11.153, p=0.027) in high-grade glioma. Our findings suggest that the hippocampus has a remarkable degree of plasticity in response to pathological stimulation of glioma and that the hippocampal reaction to glioma may be related to tumor malignancy.

A survey on machine and statistical learning for longitudinal analysis of neuroimaging data in Alzheimer's disease

BACKGROUND AND OBJECTIVES

Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging.

METHODS

We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines.

RESULTS

After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions.

CONCLUSIONS

Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.

Longitudinal change in regional brain volumes with exposure to repetitive head impacts

Objective

This study tests the hypothesis that certain MRI-based regional brain volumes will show reductions over time in a cohort exposed to repetitive head impacts (RHI).

Methods

Participants were drawn from the Professional Fighters Brain Health Study, a longitudinal observational study of professional fighters and controls. Participants underwent annual 3T brain MRI, computerized cognitive testing, and blood sampling for determination of neurofilament light (NfL) and tau levels. Yearly change in regional brain volume was calculated for several predetermined cortical and subcortical brain volumes and the relationship with NfL and tau levels determined.

Results

A total of 204 participants who had at least 2 assessments were included in the analyses. Compared to controls, the active boxers had an average yearly rate of decline in volumes of the left thalamus (102.3 mm³/y [p = 0.0004], mid anterior corpus callosum (10.2 mm³/y [p = 0.018]), and central corpus callosum (16.5 mm³/y [p = <0.0001]). Retired boxers showed the most significant volumetric declines compared to controls in left (32.1 mm³/y [p = 0.002]) and right (30.6 mm³/y [p = 0.008]) amygdala and right hippocampus (33.5 mm³/y [p = 0.01]). Higher baseline NfL levels were associated with greater volumetric decline in left hippocampus and mid anterior corpus callosum.

Conclusion

Volumetric loss in different brain regions may reflect different pathologic processes at different times among individuals exposed to RHI.

The Volume of Hippocampal Subfields in Relation to Decline of Memory Recall Across the Adult Lifespan

Background: The hippocampus is an important limbic structure closely related to memory function. However, few studies have focused on the association between hippocampal subfields and age-related memory decline. We investigated the volume alterations of hippocampal subfields at different ages and assessed the correlations with Immediate and Delayed recall abilities. Materials and Methods: A total of 275 participants aged 20-89 years were classified into 4 groups: Young, 20-35 years; Middle-early, 36-50 years; Middle-late, 51-65 years; Old, 66-89 years. All data were acquired from the Dallas Lifespan Brain Study (DLBS). The volumes of hippocampal subfields were obtained using Freesurfer software. Analysis of covariance (ANCOVA) was performed to analyze alterations of subfield volumes among the 4 groups, and multiple comparisons between groups were performed using the Bonferroni method. Spearman correlation with false discovery rate correction was used to investigate the relationship between memory recall scores and hippocampal subfield volumes. Results: Apart from no significant difference in the left parasubiculum (P = 0.269) and a slight difference in the right parasubiculum (P = 0.022), the volumes of other hippocampal subfields were significantly different across the adult lifespan (P < 0.001). The hippocampal fissure volume was increased in the Old group, while volumes for other subfields decreased. In addition, Immediate recall scores were associated with volumes of the bilateral molecular layer, granule cell layer of the dentate gyrus (GC-DG), cornus ammonis (CA) 1, CA2/3, CA4, left fimbria and hippocampal amygdala transition area (HATA), and right fissure (P < 0.05). Delayed recall scores were associated with the bilateral molecular layer, GC-DG, CA2/3 and CA4; left tail, presubiculum, CA1, subiculum, fimbria and HATA (P < 0.05). Conclusion: The parasubiculum volume was not significantly different across the adult lifespan, while atrophy in dementia patients in some studies. Based on these findings, we speculate that volume changes in this region might be considered as a biomarker for dementia disorders. Additionally, several hippocampal subfield volumes were significantly associated with memory scores, further highlighting the key role of the hippocampus in age-related memory decline. These regions could be used to assess the risk of memory decline across the adult lifespan.