MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples

BACKGROUND

The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity.

METHODS

We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection.

RESULTS

In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample.

CONCLUSION

Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data

Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, nmale /nfemale  = 110/106). The outputs of FreeSurfer's hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; RsegATLAS 2  = 65% vs. RsegFS 2  = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R2  = 25%; p < 10-3 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.

Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden

Alzheimer's disease (AD) is the most common type of dementia, characterized by early memory impairments and gradual worsening of daily functions. AD-related pathology, such as amyloid-beta (Aβ) plaques, begins to accumulate many years before the onset of clinical symptoms. Predicting risk for AD via related pathology is critical as the preclinical stage could serve as a therapeutic time window, allowing for early management of the disease and reducing health and economic costs. Current methods for detecting AD pathology, however, are often expensive and invasive, limiting wide and easy access to a clinical setting. A non-invasive, cost-efficient platform, such as computerized cognitive tests, could be potentially useful to identify at-risk individuals as early as possible. In this study, we examined the diagnostic value of an episodic memory task, the mnemonic discrimination task (MDT), for predicting risk of cognitive impairment or Aβ burden. We constructed a random forest classification algorithm, utilizing MDT performance metrics and various neuropsychological test scores as input features, and assessed model performance using area under the curve (AUC). Models based on MDT performance metrics achieved classification results with an AUC of 0.83 for cognitive status and an AUC of 0.64 for Aβ status. Our findings suggest that mnemonic discrimination function may be a useful predictor of progression to prodromal AD or increased risk of Aβ load, which could be a cost-efficient, noninvasive cognitive testing solution for potentially wide-scale assessment of AD pathological and cognitive risk.

An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies

In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.

Towards better interpretable and generalizable AD detection using collective artificial intelligence

Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the interpretability of model decisions, a lack of generalizability information and a lower performance compared to traditional machine learning techniques. In this paper, we design a two-stage framework to overcome these limitations. In the first stage, an ensemble of 125 U-Nets is used to grade the input image, producing a 3D map that reflects the disease severity at voxel-level. This map can help to localize abnormal brain areas caused by the disease. In the second stage, we model a graph per individual using the generated grading map and other information about the subject. We propose to use a graph convolutional neural network classifier for the final classification. As a result, our framework demonstrates comparative performance to the state-of-the-art methods in different datasets for both diagnosis and prognosis. We also demonstrate that the use of a large ensemble of U-Nets offers a better generalization capacity for our framework.

Temporal Irreversibility of Large-Scale Brain Dynamics in Alzheimer's Disease

Healthy brain dynamics can be understood as the emergence of a complex system far from thermodynamic equilibrium. Brain dynamics are temporally irreversible and thus establish a preferred direction in time (i.e., arrow of time). However, little is known about how the time-reversal symmetry of spontaneous brain activity is affected by Alzheimer's disease (AD). We hypothesized that the level of irreversibility would be compromised in AD, signaling a fundamental shift in the collective properties of brain activity toward equilibrium dynamics. We investigated the irreversibility from resting-state fMRI and EEG data in male and female human patients with AD and elderly healthy control subjects (HCs). We quantified the level of irreversibility and, thus, proximity to nonequilibrium dynamics by comparing forward and backward time series through time-shifted correlations. AD was associated with a breakdown of temporal irreversibility at the global, local, and network levels, and at multiple oscillatory frequency bands. At the local level, temporoparietal and frontal regions were affected by AD. The limbic, frontoparietal, default mode, and salience networks were the most compromised at the network level. The temporal reversibility was associated with cognitive decline in AD and gray matter volume in HCs. The irreversibility of brain dynamics provided higher accuracy and more distinctive information than classical neurocognitive measures when differentiating AD from control subjects. Findings were validated using an out-of-sample cohort. Present results offer new evidence regarding pathophysiological links between the entropy generation rate of brain dynamics and the clinical presentation of AD, opening new avenues for dementia characterization at different levels.SIGNIFICANCE STATEMENT By assessing the irreversibility of large-scale dynamics across multiple brain signals, we provide a precise signature capable of distinguishing Alzheimer's disease (AD) at the global, local, and network levels and different oscillatory regimes. Irreversibility of limbic, frontoparietal, default-mode, and salience networks was the most compromised by AD compared with more sensory-motor networks. Moreover, the time-irreversibility properties associated with cognitive decline and atrophy outperformed and complemented classical neurocognitive markers of AD in predictive classification performance. Findings were generalized and replicated with an out-of-sample validation procedure. We provide novel multilevel evidence of reduced irreversibility in AD brain dynamics that has the potential to open new avenues for understating neurodegeneration in terms of the temporal asymmetry of brain dynamics.

Structural brain correlates of childhood trauma with replication across two large, independent community-based samples

INTRODUCTION

Childhood trauma and adversity are common across societies and have strong associations with physical and psychiatric morbidity throughout the life-course. One possible mechanism through which childhood trauma may predispose individuals to poor psychiatric outcomes is via associations with brain structure. This study aimed to elucidate the associations between childhood trauma and brain structure across two large, independent community cohorts.

METHODS

The two samples comprised (i) a subsample of Generation Scotland (n=1,024); and (ii) individuals from UK Biobank (n=27,202). This comprised n=28,226 for mega-analysis. MRI scans were processed using Free Surfer, providing cortical, subcortical, and global brain metrics. Regression models were used to determine associations between childhood trauma measures and brain metrics and psychiatric phenotypes.

RESULTS

Childhood trauma associated with lifetime depression across cohorts (OR 1.06 GS, 1.23 UKB), and related to early onset and recurrent course within both samples. There was evidence for associations between childhood trauma and structural brain metrics. This included reduced global brain volume, and reduced cortical surface area with highest effects in the frontal (β=-0.0385, SE=0.0048, p(FDR)=5.43x10-15) and parietal lobes (β=-0.0387, SE=0.005, p(FDR)=1.56x10-14). At a regional level the ventral diencephalon (VDc) displayed significant associations with childhood trauma measures across both cohorts and at mega-analysis (β=-0.0232, SE=0.0039, p(FDR)=2.91x10-8). There were also associations with reduced hippocampus, thalamus, and nucleus accumbens volumes.

DISCUSSION

Associations between childhood trauma and reduced global and regional brain volumes were found, across two independent UK cohorts, and at mega-analysis. This provides robust evidence for a lasting effect of childhood adversity on brain structure.

Multimodal Machine Learning for 10-Year Dementia Risk Prediction: The Framingham Heart Study

BACKGROUND

Early prediction of dementia risk is crucial for effective interventions. Given the known etiologic heterogeneity, machine learning methods leveraging multimodal data, such as clinical manifestations, neuroimaging biomarkers, and well-documented risk factors, could predict dementia more accurately than single modal data.

OBJECTIVE

This study aims to develop machine learning models that capitalize on neuropsychological (NP) tests, magnetic resonance imaging (MRI) measures, and clinical risk factors for 10-year dementia prediction.

METHODS

This study included participants from the Framingham Heart Study, and various data modalities such as NP tests, MRI measures, and demographic variables were collected. CatBoost was used with Optuna hyperparameter optimization to create prediction models for 10-year dementia risk using different combinations of data modalities. The contribution of each modality and feature for the prediction task was also quantified using Shapley values.

RESULTS

This study included 1,031 participants with normal cognitive status at baseline (age 75±5 years, 55.3% women), of whom 205 were diagnosed with dementia during the 10-year follow-up. The model built on three modalities demonstrated the best dementia prediction performance (AUC 0.90±0.01) compared to single modality models (AUC range: 0.82-0.84). MRI measures contributed most to dementia prediction (mean absolute Shapley value: 3.19), suggesting the necessity of multimodal inputs.

CONCLUSION

This study shows that a multimodal machine learning framework had a superior performance for 10-year dementia risk prediction. The model can be used to increase vigilance for cognitive deterioration and select high-risk individuals for early intervention and risk management.

Leveraging Machine Learning for Gaining Neurobiological and Nosological Insights in Psychiatric Research

Much attention is currently devoted to developing diagnostic classifiers for mental disorders. Complementing these efforts, we highlight the potential of machine learning to gain biological insights into the psychopathology and nosology of mental disorders. Studies to this end have mainly used brain imaging data, which can be obtained noninvasively from large cohorts and have repeatedly been argued to reveal potentially intermediate phenotypes. This may become particularly relevant in light of recent efforts to identify magnetic resonance imaging-derived biomarkers that yield insight into pathophysiological processes as well as to refine the taxonomy of mental illness. In particular, the accuracy of machine learning models may be used as dependent variables to identify features relevant to pathophysiology. Moreover, such approaches may help disentangle the dimensional (within diagnosis) and often overlapping (across diagnoses) symptomatology of psychiatric illness. We also point out a multiview perspective that combines data from different sources, bridging molecular and system-level information. Finally, we summarize recent efforts toward a data-driven definition of subtypes or disease entities through unsupervised and semisupervised approaches. The latter, blending unsupervised and supervised concepts, may represent a particularly promising avenue toward dissecting heterogeneous categories. Finally, we raise several technical and conceptual aspects related to the reviewed approaches. In particular, we discuss common pitfalls pertaining to flawed input data or analytic procedures that would likely lead to unreliable outputs.

Depression and associated factors among older adults in Bahir Dar city administration, Northwest Ethiopia, 2020: Cross-sectional study

Background

Depression is the most common psychiatric condition among older adults, and it goes unnoticed by individuals themselves and is under-diagnosed by clinicians due to the misconception that these are normal parts of aging. However, the problem is not properly addressed in Ethiopia. This study aimed to determine the prevalence and associated factors of depression among the older adults in Bahir Dar city.

Methods

A community-based cross-sectional survey was conducted among 423 older adults in Bahir Dar city. A simple random sampling technique was used to select the study participants. Depression was assessed using a 15-item Geriatric Depression Scale (GDS). A multivariable logistic regression analysis was used to explore the potential determinants of depression among the participants.

Results

The prevalence of depression among older adults was found to be 57.9% (95% CI: 53.2–62.6). This study showed that educational status with grades 5-8^th (AOR: 5.72, 95% CI: 2.87–11.34), and 9-12^th grade (AOR: 3.44, 95% CI: 1.59–7.41), income <2004 ETB (AOR = 1.89, 95% CI: 1.16–3.07), cognitive impairments (AOR: 3.54, 95% CI: 2.16–5.81), family history of mental illness (AOR:3.06, 95% CI: 1.03–9.04), and poor quality of life (AOR: 2.78, 95% CI: 1.74–4.46) were significantly associated with depression.

Conclusion

The prevalence of depression among older adults was found to be huge. Having low educational status, low monthly income, cognitive impairments, family history of mental illness, and poor quality of life were associated with depression. Therefore, raising community awareness of mental health, increasing social participation, providing supportive counseling and routine screening of depressive symptoms are essential in combating depression among Bahir-Dar city older adults.

The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification

Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.

Specifics of the Emotional Response of Patients Suffering From Major Depressive Disorder to Imagined Basic Tastes of Food

Nowadays, the major depressive disorder (MDD) is a common disease that negatively affects the life quality of many people around the world. As MDD symptoms are closely related with the changes in food and eating, the relation between patients’ emotional responses and food tastes could be used as criteria for diagnostic. Until now, studies on the emotional response to different food tastes for patients affected by MDD have been poorly described in literature. Therefore, the aim of this study was to evaluate the emotional response of patients suffering from MDD to the imagined different food tastes and to compare the results with a control group. Emotional responses in tested participants were induced by using cards with words of basic food tastes such as “sweet,” “salty,” “bitter,” “sour,” and “neutral.” The assessment of emotional response was performed with FaceReader 6 software. The outcome of this study showed that participants with MDD expressed lower “happy” and “contempt” and higher “surprised” emotions, along with a higher negative valence mean, in comparison with controls for all tested basic tastes of food (p ≤ 0.05). When Likert scale was used, significant differences (p ≤ 0.001) in response were only found for “sour” and “salty” imaginary tastes between healthy group and patients with MDD. The findings of this study provide the additional data on food–associated emotion analysis of MDD patients and could be useful for the further development of the contactless method for early diagnosis of MDD.

Pathogeny Detection for Mild Cognitive Impairment via Weighted Evolutionary Random Forest with Brain Imaging and Genetic Data

Medical imaging technology and gene sequencing technology have long been widely used to analyze the pathogenesis and make precise diagnoses of mild cognitive impairment (MCI). However, few studies involve the fusion of radiomics data with genomics data to make full use of the complementarity between different omics to detect pathogenic factors of MCI. This paper performs multimodal fusion analysis based on functional magnetic resonance imaging (fMRI) data and single nucleotide polymorphism (SNP) data of MCI patients. In specific, first, using correlation analysis methods on sequence information of regions of interests (ROIs) and digitalized gene sequences, the fusion features of samples are constructed. Then, introducing weighted evolution strategy into ensemble learning, a novel weighted evolutionary random forest (WERF) model is built to eliminate the inefficient features. Consequently, with the help of WERF, an overall multimodal data analysis framework is established to effectively identify MCI patients and extract pathogenic factors. Based on the data of MCI patients from the ADNI database and compared with some existing popular methods, the superiority in performance of the framework is verified. Our study has great potential to be an effective tool for pathogenic factors detection of MCI.

Spectral clustering based on structural magnetic resonance imaging and its relationship with major depressive disorder and cognitive ability

There is increasing interest in using data-driven unsupervised methods to identify structural underpinnings of common mental illnesses, including major depressive disorder (MDD) and associated traits such as cognition. However, studies are often limited to severe clinical cases with small sample sizes and most do not include replication. Here, we examine two relatively large samples with structural magnetic resonance imaging (MRI), measures of lifetime MDD and cognitive variables: Generation Scotland (GS subsample, N = 980) and UK Biobank (UKB, N = 8,900), for discovery and replication, using an exploratory approach. Regional measures of FreeSurfer derived cortical thickness (CT), cortical surface area (CSA), cortical volume (CV) and subcortical volume (subCV) were input into a clustering process, controlling for common covariates. The main analysis steps involved constructing participant K-nearest neighbour graphs and graph partitioning with Markov stability to determine optimal clustering of participants. Resultant clusters were (1) checked whether they were replicated in an independent cohort and (2) tested for associations with depression status and cognitive measures. Participants separated into two clusters based on structural brain measurements in GS subsample, with large Cohen's d effect sizes between clusters in higher order cortical regions, commonly associated with executive function and decision making. Clustering was replicated in the UKB sample, with high correlations of cluster effect sizes for CT, CSA, CV and subCV between cohorts across regions. The identified clusters were not significantly different with respect to MDD case-control status in either cohort (GS subsample: p_FDR  = .2239-.6585; UKB: p_FDR  = .2003-.7690). Significant differences in general cognitive ability were, however, found between the clusters for both datasets, for CSA, CV and subCV (GS subsample: d = 0.2529-.3490, p_FDR  < .005; UKB: d = 0.0868-0.1070, p_FDR  < .005). Our results suggest that there are replicable natural groupings of participants based on cortical and subcortical brain measures, which may be related to differences in cognitive performance, but not to the MDD case-control status.

Current Views on the Role of Stress in the Pathogenesis of Chronic Neurodegenerative Diseases

The review summarizes the results of studies on the cellular and molecular mechanisms mediating the impact of stress on the pathogenesis of neurodegenerative brain pathologies (Alzheimer's disease, Parkinson's disease, etc.) and presents current information on the role of stress in the hyperphosphorylation of tau protein, aggregation of beta-amyloid, and hyperactivation of the hypothalamic-pituitary-adrenal axis involved in the hyperproduction of factors that contribute to the pathogenetic role of stress in neurodegeneration. The data on the participation of microglia in the effects of stress on the pathogenesis of neurodegenerative diseases are presented.

The mediating and moderating effects of depressive symptoms on the prospective association between cognitive function and activities of daily living disability in older adults

OBJECTIVES

This study aimed to examine to what extent depressive symptoms mediated and moderated the association between cognitive function and activities of daily living (ADL) disability in older adults.

METHODS

In older participants from the China Health and Longitudinal Retirement Survey (CHARLS), structural equation modeling and multiple regression were performed to examine the mediating and moderating role of depressive symptoms (measured by the 10-item Center for Epidemiologic Studies Depression Scale) in the association between baseline cognitive function (episodic memory, attention, orientation to time, and visuospatial ability) and endpoint disability in basic ADL (BADL) or instrumental ADL (IADL).

RESULTS

Over a 2-year follow-up, among 1677 participants (67.5 ± 6.0 years old) free of BADL disability and 1194 participants (66.9 ± 5.6 years old) free of IADL disability, 8.3% and 22.9% developed BADL disability and IADL disability, respectively. Good baseline cognitive performance was significantly associated with the reduced incidence of BADL/IADL disability. The indirect effects of baseline depressive symptoms explained 16.9% and 14.5% of the total effect between cognition and BADL and IADL dependency, respectively. The Johnson-Neyman technique identified a threshold of 7.88 for endpoint depressive symptoms, beyond which the protective effect of baseline cognitive function on BADL emerged.

CONCLUSIONS

In older adults, good cognitive function reduces the risk of BADL/IADL disability. Depressive symptoms downregulate the protective effect of cognitive function on BADL/IADL over time. Intervention techniques focusing on the simultaneous improvement of cognitive dimensions and depression help improve ADL difficulty and prevent disability in older adults.

Long-term Anabolic-Androgenic Steroid Use Is Associated With Deviant Brain Aging

BACKGROUND

High-dose long-term use of anabolic-androgenic steroids (AASs) may cause a range of adverse effects, including brain and cognitive abnormalities. We performed age prediction based on brain scans to test whether prolonged AAS use is associated with accentuated brain aging.

METHODS

T1-weighted magnetic resonance imaging (3D MPRAGE [magnetization-prepared rapid acquisition gradient-echo]) scans were obtained from male weightlifters with a history of prolonged AAS use (n = 130) or no AAS use (n = 99). We trained machine learning models on combinations of regional brain volumes, cortical thickness, and surface area in an independent training set of 1838 healthy male subjects (18-92 years of age) and predicted brain age for each participant in our study. Including cross-sectional and longitudinal (mean interval = 3.5 years, n = 76) magnetic resonance imaging data, we used linear mixed-effects models to compare the gap between chronological age and predicted brain age (the brain age gap [BAG]) for the two groups and tested for group differences in the rate of change in BAG. We tested for associations between apparent brain aging and AAS use duration, pattern of administration, and dependence.

RESULTS

AAS users had higher BAG compared with weightlifting control subjects, which was associated with dependency and longer history of use. Group differences in BAG could not be explained by other substance use, general cognitive abilities, or depression. While longitudinal analysis revealed no evidence of increased brain aging in the overall AAS group, accelerated brain aging was seen with longer AAS exposure.

CONCLUSIONS

The findings suggest that long-term high-dose AAS use may have adverse effects on brain aging, potentially linked to dependency and exaggerated use of AASs.

[Old and depressed and/or demented? - Pseudodepression vs. pseudodementia]

Many elderly patients suffer from both, depressive symptoms and cognitive deficits. Clinically, it oftentimes appears unclear whether the affective or the cognitive problems are primary or secondary. Modern molecular and imaging markers contribute to a more efficient distinction between depression and incipient dementia due to neurodegenerative, vascular, and other diseases. A careful history and clinical investigations are necessary to identify the underlying diseases, but they do not always offer sufficient therapeutic guidance. If in doubt, the condition should always be considered as potentially reversible and treated emphatically (but with age-appropriate caution).

Age-Associated Differences of Modules and Hubs in Brain Functional Networks

Healthy aging is usually accompanied by changes in the functional modular organization of the human brain, which may result in the decline of cognition and underlying brain dysfunction. However, the relationship between age-related brain functional modular structure differences and cognition remain debatable. In this study, we investigated the age-associated differences of modules and hubs from young, middle and old age groups, using resting-state fMRI data from a large cross-sectional adulthood sample. We first divided the subjects into three age groups and constructed an individual-level network for each subject. Subsequently, a module-guided group-level network construction method was applied to form a weighted network for each group from which functional modules were detected. The intra- and inter-modular connectivities were observed negatively correlated with age. According to the detected modules, we found the number of connector hubs in the young group was more than middle-age and old group, while the quantity of provincial hubs in middle-age group was discovered more than other two groups. Further ROI-wise analysis shows that different hubs have distinct age-associated trajectories of intra- and inter-modular connections, which suggests the different types of topological role transitions in functional networks across age groups. Our results indicated an inverse association between functional segregation/integration with age, which demonstrated age-associated differences in communication effeciency. This study provides a new perspective and useful information to better understand the normal aging of brain networks.

Development of Random Forest Algorithm Based Prediction Model of Alzheimer's Disease Using Neurodegeneration Pattern

OBJECTIVE

Alzheimer's disease (AD) is the most common type of dementia and the prevalence rapidly increased as the elderly population increased worldwide. In the contemporary model of AD, it is regarded as a disease continuum involving preclinical stage to severe dementia. For accurate diagnosis and disease monitoring, objective index reflecting structural change of brain is needed to correctly assess a patient's severity of neurodegeneration independent from the patient's clinical symptoms. The main aim of this paper is to develop a random forest (RF) algorithm-based prediction model of AD using structural magnetic resonance imaging (MRI).

METHODS

We evaluated diagnostic accuracy and performance of our RF based prediction model using newly developed brain segmentation method compared with the Freesurfer's which is a commonly used segmentation software.

RESULTS

Our RF model showed high diagnostic accuracy for differentiating healthy controls from AD and mild cognitive impairment (MCI) using structural MRI, patient characteristics, and cognitive function (HC vs. AD 93.5%, AUC 0.99; HC vs. MCI 80.8%, AUC 0.88). Moreover, segmentation processing time of our algorithm (<5 minutes) was much shorter than of Freesurfer's (6-8 hours).

CONCLUSION

Our RF model might be an effective automatic brain segmentation tool which can be easily applied in real clinical practice.

Detecting pathological brain via ResNet and randomized neural networks

Brain disease is one of the leading causes of death nowadays. Medical imaging is the most effective method for brain disease diagnosis, which provides a clear view of the interior brain. However, manual interpretation requires too much time and effort because medical images contain a large volume of information. Computer aided diagnosis is playing a more and more significant role in the clinic, which can help doctors and physicians to analyze medical images automatically. In this study, a novel pathological brain detection system was proposed for brain magnetic resonance images based on ResNet and randomized neural networks. Firstly, a ResNet was employed as the feature extractor, which was a famous convolutional neural network structure. Then, we used three randomized neural networks, i.e., the Schmidt neural network, the random vector functional-link net, and the extreme learning machine. The weights and biases in the three networks were trained by the chaotic bat algorithm. The three proposed methods achieved similar results based on five runs, and they yielded comparable performance in comparison with state-of-the-art approaches.

Current Cognition Tests, Potential Virtual Reality Applications, and Serious Games in Cognitive Assessment and Non-Pharmacological Therapy for Neurocognitive Disorders

As the global population ages, the incidence of major neurocognitive disorders (major NCDs), such as the most common geriatric major NCD, Alzheimer's disease (AD), has grown. Thus, the need for more definitive cognitive assessment or even effective non-pharmacological intervention for age-related NCDs is becoming more and more pressing given that no definitive diagnostics or efficacious therapeutics are currently unavailable for them. We evaluate the current state of the art of cognitive assessment for major NCDs, and then briefly glance ahead at potential application of virtual reality (VR) technologies in major NCD assessment and in cognition training of visuospatial reasoning in a 3D environment, as well as in the alleviation of depression and other symptoms of cognitive disorders. We believe that VR-based technologies have tremendous potentials in cognitive assessment and non-pharmacological therapy for major NCDs.

Predicting conversion to Alzheimer's disease among individual high-risk patients using the Characterizing AD Risk Events index model

AIMS

Both amnestic mild cognitive impairment (aMCI) and remitted late-onset depression (rLOD) confer a high risk of developing Alzheimer's disease (AD). This study aims to determine whether the Characterizing AD Risk Events (CARE) index model can effectively predict conversion in individuals at high risk for AD development either in an independent aMCI population or in an rLOD population.

METHODS

The CARE index model was constructed based on the event-based probabilistic framework fusion of AD biomarkers to differentiate individuals progressing to AD from cognitively stable individuals in the aMCI population (27 stable subjects, 6 progressive subjects) and rLOD population (29 stable subjects, 10 progressive subjects) during the follow-up period.

RESULTS

AD diagnoses were predicted in the aMCI population with a balanced accuracy of 80.6%, a sensitivity of 83.3%, and a specificity of 77.8%. They were also predicted in the rLOD population with a balanced accuracy of 74.5%, a sensitivity of 80.0%, and a specificity of 69.0%. In addition, the CARE index scores were observed to be negatively correlated with the composite Z scores for episodic memory (R2  = .17, P < .001) at baseline in the combined high-risk population (N = 72).

CONCLUSIONS

The CARE index model can be used for the prediction of conversion to AD in both aMCI and rLOD populations effectively. Additionally, it can be used to monitor the disease severity of patients.

Brain Structure in Bilingual Compared to Monolingual Individuals with Alzheimer's Disease: Proof of Concept

BACKGROUND

Bilingualism is increasingly recognized as protective in persons at risk for Alzheimer's disease (AD).

OBJECTIVE

Compare MRI measured brain volumes in matched bilinguals versus monolinguals with AD.

METHODS

This IRB approved study analyzed T1 volumetric brain MRIs of patients with criteria-supported Probable AD. We identified 17 sequential bilinguals (any native language) with Probable AD, matched to 28 (62%) monolinguals on age and MMSE. Brain volumes were quantified with Neuroreader. Regional volumes as fraction of total intracranial volume (TIV) were compared between both groups, and Cohen's D effect sizes were calculated for statistically significant structures. Partial correlations between bilingualism and brain volumes adjusted for age, gender, and TIV.

RESULTS

Bilinguals had higher brain volumes in 37 structures. Statistical significance (p < 0.05) was observed in brainstem (t = 2.33, p = 0.02, Cohen's D = 0.71) and ventral diencephalon (t = 3.01, p = 0.004, Cohen's D = 0.91). Partial correlations showed statistical significance between bilingualism and larger volumes in brainstem (rp = 0 . 37, p = 0.01), thalamus (rp = 0.31, p = 0.04), ventral diencephalon (rp = 0.50, p = 0.001), and pallidum (rp = 0.38, p = 0.01). Bilingualism positively correlated with hippocampal volume, though not statistically significant (rp = 0.17, p = 0.26). No brain volumes were larger in monolinguals.

CONCLUSION

Bilinguals demonstrated larger thalamic, ventral diencephalon, and brainstem volumes compared to matched monolinguals with AD. This may represent a neural substrate for increased cognitive reserve in bilingualism. Future studies should extrapolate this finding into cognitively normal persons at risk for AD.

Corticotropin releasing factor-binding protein (CRF-BP) as a potential new therapeutic target in Alzheimer's disease and stress disorders

Alzheimer's disease is the most common cause of dementia and one of the most complex human neurodegenerative diseases. Numerous studies have demonstrated a critical role of the environment in the pathogenesis and pathophysiology of the disease, where daily life stress plays an important role. A lot of epigenetic studies have led to the conclusion that chronic stress and stress-related disorders play an important part in the onset of neurodegenerative disorders, and an enormous amount of research yielded valuable discoveries but has so far not led to the development of effective treatment strategies for Alzheimer's disease. Corticotropin-releasing factor (CRF) is one of the major hormones and at the same time a neuropeptide acting in stress response. Deregulation of protein levels of CRF is involved in the pathogenesis of Alzheimer's disease, but little is known about the precise roles of CRF and its binding protein, CRF-BP, in neurodegenerative diseases. In this review, we summarize the key evidence for and against the involvement of stress-associated modulation of the CRF system in the pathogenesis of Alzheimer's disease and discuss how recent findings could lead to new potential treatment possibilities in Alzheimer's disease by using CRF-BP as a therapeutic target.

Early diagnosis of Alzheimer's disease using combined features from voxel-based morphometry and cortical, subcortical, and hippocampus regions of MRI T1 brain images

In recent years, several high-dimensional, accurate, and effective classification methods have been proposed for the automatic discrimination of the subject between Alzheimer's disease (AD) or its prodromal phase {i.e., mild cognitive impairment (MCI)} and healthy control (HC) persons based on T1-weighted structural magnetic resonance imaging (sMRI). These methods emphasis only on using the individual feature from sMRI images for the classification of AD, MCI, and HC subjects and their achieved classification accuracy is low. However, latest multimodal studies have shown that combining multiple features from different sMRI analysis techniques can improve the classification accuracy for these types of subjects. In this paper, we propose a novel classification technique that precisely distinguishes individuals with AD, aAD (stable MCI, who had not converted to AD within a 36-month time period), and mAD (MCI caused by AD, who had converted to AD within a 36-month time period) from HC individuals. The proposed method combines three different features extracted from structural MR (sMR) images using voxel-based morphometry (VBM), hippocampal volume (HV), and cortical and subcortical segmented region techniques. Three classification experiments were performed (AD vs. HC, aAD vs. mAD, and HC vs. mAD) with 326 subjects (171 elderly controls and 81 AD, 35 aAD, and 39 mAD patients). For the development and validation of the proposed classification method, we acquired the sMR images from the dataset of the National Research Center for Dementia (NRCD). A five-fold cross-validation technique was applied to find the optimal hyperparameters for the classifier, and the classification performance was compared by using three well-known classifiers: K-nearest neighbor, support vector machine, and random forest. Overall, the proposed model with the SVM classifier achieved the best performance on the NRCD dataset. For the individual feature, the VBM technique provided the best results followed by the HV technique. However, the use of combined features improved the classification accuracy and predictive power for the early classification of AD compared to the use of individual features. The most stable and reliable classification results were achieved when combining all extracted features. Additionally, to analyze the efficiency of the proposed model, we used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to compare the classification performance of the proposed model with those of several state-of-the-art methods.

A practical computerized decision support system for predicting the severity of Alzheimer's disease of an individual

Computerized clinical decision support systems can help to provide objective, standardized, and timely dementia diagnosis. However, current computerized systems are mainly based on group analysis, discrete classification of disease stages, or expensive and not readily accessible biomarkers, while current clinical practice relies relatively heavily on cognitive and functional assessments (CFA). In this study, we developed a computational framework using a suite of machine learning tools for identifying key markers in predicting the severity of Alzheimer's disease (AD) from a large set of biological and clinical measures. Six machine learning approaches, namely Kernel Ridge Regression (KRR), Support Vector Regression, and k-Nearest Neighbor for regression and Support Vector Machine (SVM), Random Forest, and k-Nearest Neighbor for classification, were used for the development of predictive models. We demonstrated high predictive power of CFA. Predictive performance of models incorporating CFA was shown to consistently have higher accuracy than those based solely on biomarker modalities. We found that KRR and SVM were the best performing regression and classification methods respectively. The optimal SVM performance was observed for a set of four CFA test scores (FAQ, ADAS13, MoCA, MMSE) with multi-class classification accuracy of 83.0%, 95%CI = (72.1%, 93.8%) while the best performance of the KRR model was reported with combined CFA and MRI neuroimaging data, i.e., R ² = 0.874, 95%CI = (0.827, 0.922). Given the high predictive power of CFA and their widespread use in clinical practice, we then designed a data-driven and self-adaptive computerized clinical decision support system (CDSS) prototype for evaluating the severity of AD of an individual on a continuous spectrum. The system implemented an automated computational approach for data pre-processing, modelling, and validation and used exclusively the scores of selected cognitive measures as data entries. Taken together, we have developed an objective and practical CDSS to aid AD diagnosis.

The effect of white matter hyperintensities on regional brain volumes and white matter microstructure, a population-based study in HUNT

Even though age-related white matter hyperintensities (WMH) begin to emerge in middle age, their effect on brain micro- and macrostructure in this age group is not fully elucidated. We have examined how presence of WMH and load of WMH affect regional brain volumes and microstructure in a validated, representative general population sample of 873 individuals between 50 and 66 years. Presence of WMH was determined as Fazakas grade ≥1. WMH load was WMH volume from manual tracing of WMHs divided on intracranial volume. The impact of age appropriate WMH (Fazakas grade 1) on the brain was also investigated. Major novel findings were that even the age appropriate WMH group had widespread macro- and microstructural changes in gray and white matter, showing that the mere presence of WMH, not just WMH load is an important clinical indicator of brain health. With increasing WMH load, structural changes spread centrifugally. Further, we found three major patterns of FA and MD changes related to increasing WMH load, demonstrating a heterogeneous effect on white matter microstructure, where distinct patterns were found in the proximity of the lesions, in deep white matter and in white matter near the cortex. This study also raises several questions about the onset of WMH related pathology, in particular, whether some of the aberrant brain structural and microstructural findings are present before the emergence of WMH. We also found, similar to other studies, that WMH risk factors had low explanatory power for WMH, making it unclear which factors lead to WMH.

Progress in psychoradiology, the clinical application of psychiatric neuroimaging

Psychoradiology is an emerging field that applies radiological imaging technologies to psychiatric conditions. In the past three decades, brain imaging techniques have rapidly advanced understanding of illness and treatment effects in psychiatry. Based on these advances, radiologists have become increasingly interested in applying these advances for differential diagnosis and individualized patient care selection for common psychiatric illnesses. This shift from research to clinical practice represents the beginning evolution of psychoradiology. In this review, we provide a summary of recent progress relevant to this field based on their clinical functions, namely the (1) classification and subtyping; (2) prediction and monitoring of treatment outcomes; and (3) treatment selection. In addition, we provide guidelines for the practice of psychoradiology in clinical settings and suggestions for future research to validate broader clinical applications. Given the high prevalence of psychiatric disorders and the importance of increased participation of radiologists in this field, a guide regarding advances in this field and a description of relevant clinical work flow patterns help radiologists contribute to this fast-evolving field.

Diagnosis of Alzheimer's Disease via Multi-Modality 3D Convolutional Neural Network

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. In the last decade, studies on AD diagnosis has attached great significance to artificial intelligence-based diagnostic algorithms. Among the diverse modalities of imaging data, T1-weighted MR and FDG-PET are widely used for this task. In this paper, we propose a convolutional neural network (CNN) to integrate all the multi-modality information included in both T1-MR and FDG-PET images of the hippocampal area, for the diagnosis of AD. Different from the traditional machine learning algorithms, this method does not require manually extracted features, instead, it utilizes 3D image-processing CNNs to learn features for the diagnosis or prognosis of AD. To test the performance of the proposed network, we trained the classifier with paired T1-MR and FDG-PET images in the ADNI datasets, including 731 cognitively unimpaired (labeled as CN) subjects, 647 subjects with AD, 441 subjects with stable mild cognitive impairment (sMCI) and 326 subjects with progressive mild cognitive impairment (pMCI). We obtained higher accuracies of 90.10% for CN vs. AD task, 87.46% for CN vs. pMCI task, and 76.90% for sMCI vs. pMCI task. The proposed framework yields a state-of-the-art performance. Finally, the results have demonstrated that (1) segmentation is not a prerequisite when using a CNN for the classification, (2) the combination of two modality imaging data generates better results.

Lifetime major depression and grey-matter volume

BACKGROUND

There is evidence of structural brain alterations in major depressive disorder (MDD), but little is known about how these alterations might be affected by age at onset or genetic vulnerability. This study examines whether lifetime episodes of MDD are associated with specific alterations in grey-matter volume, and whether those alterations vary according to sex or serotonin transporter-linked promoter region (5-HTTLPR) genotype (LL, SL or SS).

METHODS

We used structural MRI to acquire anatomic scans from 610 community-dwelling participants. We derived quantitative regional estimates of grey-matter volume in 16 subregions using FreeSurfer software. We diagnosed MDD according to DSM-IV criteria. We adjusted analyses for age, sex, total brain volume, education level, head injury and comorbidities.

RESULTS

Lifetime MDD was associated with a smaller insula, thalamus, ventral diencephalon, pallidum and nucleus accumbens and with a larger pericalcarine region in both men and women. These associations remained after adjustment for false discovery rate. Lifetime MDD was also associated with a smaller caudate nucleus and amygdala in men and with a larger rostral anterior cingulate cortex in women. Late-onset first episodes of MDD (after age 50 years) were associated with a larger rostral anterior cingulate cortex and lingual and pericalcarine regions; early-onset MDD was associated with a smaller ventral diencephalon and nucleus accumbens. Some associations differed according to 5-HTTLPR genotype: the thalamus was smaller in participants with MDD and the LL genotype; pericalcarine and lingual volumes were higher in those with the SL genotype.

LIMITATIONS

This study was limited by its cross-sectional design.

CONCLUSION

Major depressive disorder was associated with persistent volume reductions in the deep nuclei and insula and with enlargements in visual cortex subregions; alterations varied according to age of onset and genotype.

Multiplex Networks for Early Diagnosis of Alzheimer's Disease

Analysis and quantification of brain structural changes, using Magnetic Resonance Imaging (MRI), are increasingly used to define novel biomarkers of brain pathologies, such as Alzheimer's disease (AD). Several studies have suggested that brain topological organization can reveal early signs of AD. Here, we propose a novel brain model which captures both intra- and inter-subject information within a multiplex network approach. This model localizes brain atrophy effects and summarizes them with a diagnostic score. On an independent test set, our multiplex-based score segregates (i) normal controls (NC) from AD patients with a 0.86±0.01 accuracy and (ii) NC from mild cognitive impairment (MCI) subjects that will convert to AD (cMCI) with an accuracy of 0.84±0.01. The model shows that illness effects are maximally detected by parceling the brain in equal volumes of 3, 000 mm3 ("patches"), without any a priori segmentation based on anatomical features. The multiplex approach shows great sensitivity in detecting anomalous changes in the brain; the robustness of the obtained results is assessed using both voxel-based morphometry and FreeSurfer morphological features. Because of its generality this method can provide a reliable tool for clinical trials and a disease signature of many neurodegenerative pathologies.

Prevalence and associated factors of depressive symptoms among elderly inpatients of a Chinese tertiary hospital

BACKGROUND

Depression in the elderly is a serious and often underdiagnosed psychiatric disorder that has been linked to adverse outcomes in the hospital setting. This study aims to evaluate the prevalence of depressive symptoms and associated factors among elderly hospital inpatients.

METHODS

The cross-sectional study included 411 consecutively hospitalized patients aged 60 years and older. Participants were evaluated within 48 hours of admission using an interviewer-administered questionnaire including the Geriatric Depression Scale and comprehensive geriatric assessment to provide basic demographic and clinical information.

RESULTS

Most of the participants were male (64.5%), with a mean (SD) age of 75.9 (8.1) years between 60 and 97 years. The prevalence of depressive symptoms was 32.8%. Univariate analysis showed significant associations between depressive symptoms and older age, female gender, lower body mass index, number of chronic diseases, impaired family function, impaired cognition, malnutrition, increased frailty, and decreased ability to perform activities of daily living. After logistic regression, variables that remained significantly associated with depression were cognitive decline (odds ratio =1.97, 95% CI: 1.09-3.55), poor family function (odds ratio =2.01, 95% CI: 1.10-3.66), and frailty (odds ratio =5.07, 95% CI: 1.95-13.20). Depressive symptoms were independently associated with prolonged hospital length.

CONCLUSION

Depressive symptoms were prevalent among hospitalized elderly and independently associated with cognitive decline, poor family function, and frailty. Therefore, it is essential to screen for depression and perform a comprehensive geriatric assessment in these patients to identify and manage depressive symptoms.

Using Artificial Neural Networks for Identifying Patients with Mild Cognitive Impairment Associated with Depression Using Neuropsychological Test Features

Depression and cognitive impairment are intimately associated, especially in elderly people. However, the association between late-life depression (LLD) and mild cognitive impairment (MCI) is complex and currently unclear. In general, it can be said that LLD and cognitive impairment can be due to a common cause, such as a vascular disease, or simply co-exist in time but have different causes. To contribute to the understanding of the evolution and prognosis of these two diseases, this study’s primary intent was to explore the ability of artificial neural networks (ANNs) to identify an MCI subtype associated with depression as an entity by using the scores of an extensive neurological examination. The sample consisted of 96 patients classified into two groups: 42 MCI with depression and 54 MCI without depression. According to our results, ANNs can identify an MCI that is highly associated with depression distinguishable from the non-depressed MCI patients (accuracy = 86%, sensitivity = 82%, specificity = 89%). These results provide data in favor of a cognitive frontal profile of patients with LLD, distinct and distinguishable from other cognitive impairments. Therefore, it should be taken into account in the classification of MCI subtypes for future research, including depression as an essential variable in the classification of a patient with cognitive impairment.

The Role of Physical Exercise and Omega-3 Fatty Acids in Depressive Illness in the Elderly

Background:

In adulthood, depression is the most common type of mental illness and will be the second leading cause of disease by 2020. Major depression dramatically affects the function of the central nervous system and degrades the quality of life, especially in old age.

Several mechanisms underlie the pathophysiology of depressive illness, since it has a multifactorial etiology. Human and an-imal studies have demonstrated that depression is mainly associated with imbalances in neurotransmitters and neurotrophins, hypothalamic-pituitary-adrenal axis alterations, brain volume changes, neurogenesis dysfunction, and dysregulation of in-flammatory pathways. Also the gut microbiota may influence mental health outcomes.

Although depression is not a consequence of normal aging, depressive disorders are common in later life, even if often undi-agnosed or mis-diagnosed in old age. When untreated, depression reduces life expectancy, worsens medical illnesses, en-hances health care costs and is the primary cause of suicide among older people. To date, the underpinnings of depression in the elderly are still to be understood, and the pharmacological treatment is the most commonly used therapy.

Objective:

Since a sedentary lifestyle and poor eating habits have recently emerged as crucial contributors to the genesis and course of depression, in the present review, we have focused on the effects of physical activity and omega-3 fatty acids on depressive illness in the elderly.

Results:

A growing literature indicates that both exercise and dietary interventions can promote mental health throughout one’s lifespan.

Conclusion:

There thus emerges the awareness that an active lifestyle and a balanced diet may constitute valid low-cost pre-vention strategies to counteract depressive illness in the elderly.

How random is the random forest? Random forest algorithm on the service of structural imaging biomarkers for Alzheimer's disease: from Alzheimer's disease neuroimaging initiative (ADNI) database

Neuroinformatics is a fascinating research field that applies computational models and analytical tools to high dimensional experimental neuroscience data for a better understanding of how the brain functions or dysfunctions in brain diseases. Neuroinformaticians work in the intersection of neuroscience and informatics supporting the integration of various sub-disciplines (behavioural neuroscience, genetics, cognitive psychology, etc.) working on brain research. Neuroinformaticians are the pathway of information exchange between informaticians and clinicians for a better understanding of the outcome of computational models and the clinical interpretation of the analysis. Machine learning is one of the most significant computational developments in the last decade giving tools to neuroinformaticians and finally to radiologists and clinicians for an automatic and early diagnosis-prognosis of a brain disease. Random forest (RF) algorithm has been successfully applied to high-dimensional neuroimaging data for feature reduction and also has been applied to classify the clinical label of a subject using single or multi-modal neuroimaging datasets. Our aim was to review the studies where RF was applied to correctly predict the Alzheimer's disease (AD), the conversion from mild cognitive impairment (MCI) and its robustness to overfitting, outliers and handling of non-linear data. Finally, we described our RF-based model that gave us the 1st position in an international challenge for automated prediction of MCI from MRI data.

Separating Symptomatic Alzheimer's Disease from Depression based on Structural MRI

Older patients with depression or Alzheimer’s disease (AD) at the stage of early dementia or mild cognitive impairment may present with objective cognitive impairment, although the pathology and thus therapy and prognosis differ substantially. In this study, we assessed the potential of an automated algorithm to categorize a test set of 65 T1-weighted structural magnetic resonance images (MRI). A convenience sample of elderly individuals fulfilling clinical criteria of either AD (n = 28) or moderate and severe depression (n = 37) was recruited from different settings to assess the potential of the pattern recognition method to assist in the differential diagnosis of AD versus depression. We found that our algorithm learned discriminative patterns in the subject’s grey matter distribution reflected by an area under the receiver operator characteristics curve of up to 0.83 (confidence interval ranged from 0.67 to 0.92) and a balanced accuracy of 0.79 for the separation of depression from AD, evaluated by leave-one-out cross validation. The algorithm also identified consistent structural differences in a clinically more relevant scenario where the data used during training were independent from the data used for evaluation and, critically, which included five possible diagnoses (specifically AD, frontotemporal dementia, Lewy body dementia, depression, and healthy aging). While the output was insufficiently accurate to use it directly as a means for classification when multiple classes are possible, the continuous output computed by the machine learning algorithm differed between the two groups that were investigated. The automated analysis thus could complement, but not replace clinical assessments.

The relationship between stress and Alzheimer's disease

Stress is critically involved in the development and progression of disease. From the stress of undergoing treatments to facing your own mortality, the physiological processes that stress drives have a serious detrimental effect on the ability to heal, cope and maintain a positive quality of life. This is becoming increasingly clear in the case of neurodegenerative diseases. Neurodegenerative diseases involve the devastating loss of cognitive and motor function which is stressful in itself, but can also disrupt neural circuits that mediate stress responses. Disrupting these circuits produces aberrant emotional and aggressive behavior that causes long-term care to be especially difficult. In addition, added stress drives progression of the disease and can exacerbate symptoms. In this review, I describe how neural and endocrine pathways activated by stress interact with ongoing neurodegenerative disease from both a clinical and experimental perspective.

The Perspectives Associated With the Computer-Based Diagnostic Method of Depressive Disorder

Depressive disorder (DD) shortens a healthy and productive human life, has significant public health costs is and associated with high suicide rates. In depression sadness and emotional misery manifest in facial expressions, as psychomotor slowing, lack of energy, high tension, and attenuated sensory perception. Loss of appetite, changes to the taste of food, and the loss of pleasure in eating are important criteria in the diagnosis of DD. We hypothesized that a patient's facial expressions and emotional responses to different tastes can be used as the diagnostic moderators for the development of a new contactless, computer-based method for diagnosis of DD. The confirmation of this hypothesis can shed a new perspective on early contactless, computer-based psychiatric diagnostic strategies and early identification of DD symptoms, as DD is an important issue in public mental health. The benefits of this method are evidence from several perspectives (I) patients can use a self-rating instrument to assess DD symptoms; this may act as an incentive to seek professional help; (II) family and community can use an instrument for early recognition of DD symptoms and suicidal tendencies, making it possible to encourage the individual to seek professional health care; (III) general practitioners have a reliable instrument for preliminary diagnosis of DD in primary care, thus saving the time and resources; (IV) public health benefits include early diagnosis and treatment of DD and better outcomes, reductions in disability-adjusted life years and the global burden of the disease. It is nevertheless important to recognize the limitations and risks of contactless diagnosis of DD. As it is a self-assessment method it is not possible to rule out false positives and false negatives. However, this method might be used for early diagnosis of DD symptoms. Also, it should be mentioned that further evaluation and an experts opinion about this method is needed. The clinical diagnosis of DD should continue to be made by healthcare professionals. Finally, this method may perspectively predict DD at an early stage and may ensure a higher quality of the patients' primary care in the public health system.

Detection of cognitive impairment using a machine-learning algorithm

PURPOSE

The Mini-Mental State Examination (MMSE) is one of the most frequently used bedside screening measures of cognition. However, the Korean Dementia Screening Questionnaire (KDSQ) is an easier and more reliable screening method. Instead, other clinical variables and raw data were used for this study without the consideration of a cutoff value. The objective of this study was to develop a machine-learning algorithm for the detection of cognitive impairment (CI) based on the KDSQ and the MMSE.

PATIENTS AND METHODS

The original dataset from the Clinical Research Center for Dementia of South Korea study was obtained. In total, 9,885 and 300 patients were randomly allocated to the training and test datasets, respectively. We selected up to 24 variables including sex, age, education duration, diabetes mellitus, and hypertension. We trained a machine-learning algorithm using TensorFlow based on the training dataset and then calculated its accuracy using the test dataset. The cost was calculated by conducting a logistic regression.

RESULTS

The accuracy of the model in predicting CI based on the KDSQ only, the MMSE only, and the combination of the KDSQ and MMSE was 84.3%, 88.3%, and 86.3%, respectively. For the KDSQ, the sensitivity for detecting CI was 91.50% and the specificity for detecting normal cognition (NL) was 59.60%. The sensitivity of the MMSE was 94.35%, and the specificity was 59.62%. When combining the KDSQ and the MMSE, the sensitivity for detecting CI was 91.5% and the specificity for detecting NL was 61.5%.

CONCLUSION

The algorithm predicting CI based on the MMSE is superior. However, the KDSQ can be administered more easily in clinical practice and the algorithm using KDSQ is a comparable screening tool.

Abnormal Default Mode Network Homogeneity in Treatment-Naive Patients With First-Episode Depression

Background and Objective: The default mode network (DMN) may be an important component involved in the broad-scale cognitive problems seen in patients with first-episode treatment-naive depression. Nevertheless, information is scarce regarding the changes in network homogeneity (NH) found in the DMN of these patients. Therefore, in this study, we explored the NH of the DMN in patients with first-episode treatment-naive depression. Methods: The study included 66 patients and 74 control participants matched by age, gender, educational level and health status who underwent resting-state functional magnetic resonance imaging (rs-fMRI) and the attentional network test (ANT). To assess data, the study utilizes NH and independent component analysis (ICA). Additionally, Spearman's rank correlation analysis is performed among significantly abnormal NH in depression patients and clinical measurements and executive control reaction time (ECRT). Results: In comparison with the control group, patients with first-episode treatment-naive depression showed lower NH in the bilateral angular gyrus (AG), as well as increased NH in the bilateral precuneus (PCu) and posterior cingulate cortex (PCC). Likewise, patients with first-episode treatment-naive depression had longer ECRT. No significant relation was found between abnormal NH values and the measured clinical variables. Conclusions: Our results suggest patients with first-episode treatment-naive depression have abnormal NH values in the DMN. This highlights the significance of DMN in the pathophysiology of cognitive problems in depression. Our study also found alterations in executive functions in patients with first-episode treatment-naive depression.

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database

BACKGROUND

In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using features derived from a single modality, namely MRI.

NEW METHOD

Based on preprocessed MRI images from the organizers of a neuroimaging challenge,³ we attempted to quantify the prediction accuracy of multiple morphological MRI features to simultaneously discriminate among HC, MCI, cMCI and AD. We explored the efficacy of a novel scheme that includes multiple feature selections via Random Forest from subsets of the whole set of features (e.g. whole set, left/right hemisphere etc.), Random Forest classification using a fusion approach and ensemble classification via majority voting. From the ADNI database, 60 HC, 60 MCI, 60 cMCI and 60 CE were used as a training set with known labels. An extra dataset of 160 subjects (HC: 40, MCI: 40, cMCI: 40 and AD: 40) was used as an external blind validation dataset to evaluate the proposed machine learning scheme.

RESULTS

In the second blind dataset, we succeeded in a four-class classification of 61.9% by combining MRI-based features with a Random Forest-based Ensemble Strategy. We achieved the best classification accuracy of all teams that participated in this neuroimaging competition.

COMPARISON WITH EXISTING METHOD(S)

The results demonstrate the effectiveness of the proposed scheme to simultaneously discriminate among four groups using morphological MRI features for the very first time in the literature.

CONCLUSIONS

Hence, the proposed machine learning scheme can be used to define single and multi-modal biomarkers for AD.

Decreased Complexity in Alzheimer's Disease: Resting-State fMRI Evidence of Brain Entropy Mapping

Alzheimer's disease (AD) is a frequently observed, irreversible brain function disorder among elderly individuals. Resting-state functional magnetic resonance imaging (rs-fMRI) has been introduced as an alternative approach to assessing brain functional abnormalities in AD patients. However, alterations in the brain rs-fMRI signal complexities in mild cognitive impairment (MCI) and AD patients remain unclear. Here, we described the novel application of permutation entropy (PE) to investigate the abnormal complexity of rs-fMRI signals in MCI and AD patients. The rs-fMRI signals of 30 normal controls (NCs), 33 early MCI (EMCI), 32 late MCI (LMCI), and 29 AD patients were obtained from the Alzheimer's disease Neuroimaging Initiative (ADNI) database. After preprocessing, whole-brain entropy maps of the four groups were extracted and subjected to Gaussian smoothing. We performed a one-way analysis of variance (ANOVA) on the brain entropy maps of the four groups. The results after adjusting for age and sex differences together revealed that the patients with AD exhibited lower complexity than did the MCI and NC controls. We found five clusters that exhibited significant differences and were distributed primarily in the occipital, frontal, and temporal lobes. The average PE of the five clusters exhibited a decreasing trend from MCI to AD. The AD group exhibited the least complexity. Additionally, the average PE of the five clusters was significantly positively correlated with the Mini-Mental State Examination (MMSE) scores and significantly negatively correlated with Functional Assessment Questionnaire (FAQ) scores and global Clinical Dementia Rating (CDR) scores in the patient groups. Significant correlations were also found between the PE and regional homogeneity (ReHo) in the patient groups. These results indicated that declines in PE might be related to changes in regional functional homogeneity in AD. These findings suggested that complexity analyses using PE in rs-fMRI signals can provide important information about the fMRI characteristics of cognitive impairments in MCI and AD.

Random Forest Algorithm for the Classification of Neuroimaging Data in Alzheimer's Disease: A Systematic Review

Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer's disease.

Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: (“random forest” OR “random forests”) AND neuroimaging AND (“alzheimer's disease” OR alzheimer's OR alzheimer) AND (prediction OR classification). The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science.

Results: Twelve articles—published between the 2007 and 2017—have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer's disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as, MRI morphometric, diffusion tensor imaging, and PET images.

Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

Classifying MCI Subtypes in Community-Dwelling Elderly Using Cross-Sectional and Longitudinal MRI-Based Biomarkers

Amnestic MCI (aMCI) and non-amnestic MCI (naMCI) are considered to differ in etiology and outcome. Accurately classifying MCI into meaningful subtypes would enable early intervention with targeted treatment. In this study, we employed structural magnetic resonance imaging (MRI) for MCI subtype classification. This was carried out in a sample of 184 community-dwelling individuals (aged 73-85 years). Cortical surface based measurements were computed from longitudinal and cross-sectional scans. By introducing a feature selection algorithm, we identified a set of discriminative features, and further investigated the temporal patterns of these features. A voting classifier was trained and evaluated via 10 iterations of cross-validation. The best classification accuracies achieved were: 77% (naMCI vs. aMCI), 81% (aMCI vs. cognitively normal (CN)) and 70% (naMCI vs. CN). The best results for differentiating aMCI from naMCI were achieved with baseline features. Hippocampus, amygdala and frontal pole were found to be most discriminative for classifying MCI subtypes. Additionally, we observed the dynamics of classification of several MRI biomarkers. Learning the dynamics of atrophy may aid in the development of better biomarkers, as it may track the progression of cognitive impairment.