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Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects
Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures
Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects
Multimodal brain age estimates relate to Alzheimer disease biomarkers and cognition in early stages: a cross-sectional observational study
BACKGROUND: Estimates of 'brain-predicted age' quantify apparent brain age compared to normative trajectories of neuroimaging features. The brain age gap (BAG) between predicted and chronological age is elevated in symptomatic Alzheimer disease (AD) but has not been well explored in presymptomatic AD. Prior studies have typically modeled BAG with structural MRI, but more recently other modalities, including functional connectivity (FC) and multimodal MRI, have been explored. METHODS: We trained three models to predict age from FC, structural (S), or multimodal MRI (S+FC) in 390 amyloid-negative cognitively normal (CN/A-) participants (18-89 years old). In independent samples of 144 CN/A-, 154 CN/A+, and 154 cognitively impaired (CI; CDR > 0) participants, we tested relationships between BAG and AD biomarkers of amyloid and tau, as well as a global cognitive composite. RESULTS: All models predicted age in the control training set, with the multimodal model outperforming the unimodal models. All three BAG estimates were significantly elevated in CI compared to controls. FC-BAG was significantly reduced in CN/A+ participants compared to CN/A-. In CI participants only, elevated S-BAG and S+FC BAG were associated with more advanced AD pathology and lower cognitive performance. CONCLUSIONS: Both FC-BAG and S-BAG are elevated in CI participants. However, FC and structural MRI also capture complementary signals. Specifically, FC-BAG may capture a unique biphasic response to presymptomatic AD pathology, while S-BAG may capture pathological progression and cognitive decline in the symptomatic stage. A multimodal age-prediction model improves sensitivity to healthy age differences. FUNDING: This work was supported by the National Institutes of Health (P01-AG026276, P01- AG03991, P30-AG066444, 5-R01-AG052550, 5-R01-AG057680, 1-R01-AG067505, 1S10RR022984-01A1, and U19-AG032438), the BrightFocus Foundation (A2022014F), and the Alzheimer's Association (SG-20-690363-DIAN)
BlindHarmony: "Blind" Harmonization for MR Images via Flow model
In MRI, images of the same contrast (e.g., T) from the same subject can
exhibit noticeable differences when acquired using different hardware,
sequences, or scan parameters. These differences in images create a domain gap
that needs to be bridged by a step called image harmonization, to process the
images successfully using conventional or deep learning-based image analysis
(e.g., segmentation). Several methods, including deep learning-based
approaches, have been proposed to achieve image harmonization. However, they
often require datasets from multiple domains for deep learning training and may
still be unsuccessful when applied to images from unseen domains. To address
this limitation, we propose a novel concept called `Blind Harmonization', which
utilizes only target domain data for training but still has the capability to
harmonize images from unseen domains. For the implementation of blind
harmonization, we developed BlindHarmony using an unconditional flow model
trained on target domain data. The harmonized image is optimized to have a
correlation with the input source domain image while ensuring that the latent
vector of the flow model is close to the center of the Gaussian distribution.
BlindHarmony was evaluated on both simulated and real datasets and compared to
conventional methods. BlindHarmony demonstrated noticeable performance on both
datasets, highlighting its potential for future use in clinical settings. The
source code is available at: https://github.com/SNU-LIST/BlindHarmonyComment: ICCV 2023 accepted. 9 pages and 5 Figures for manuscipt,
supplementary include
Clinical and neuroimaging prognostic markers in Alzheimer's Disease and Lewy Body Dementia: The role of muscle status and nutrition
Alzheimer's Disease and Lewy body dementia are the two most common neurodegenerative dementias. They have a progressive course with devastating consequences for the people living with these diseases and their families, but there are large individual variations. Finding early markers and markers of progression and prognosis could promote actions to improve the quality of life of the people affected with these diseases. Nutrition and muscle status are closely related and have systemic functions and interactions that affect the brain. This thesis describes the role of nutritional and muscle status biomarkers in the prognosis of people diagnosed with mild Alzheimer's disease, Lewy body dementia, and mild subjective cognitive decline.
Methods
For the aim of this thesis, I used data from 2 community-based prospective Norwegian multicenter cohort studies: DemVest (The Dementia Study of Western Norway) and DDI (Dementia Disease Initiation). In DemVest, patients with mild dementia, defined as a Mini-Mental Status Examination (MMSE) score; equal or higher to 20 or Clinical Dementia Rating (CDR) global score equal to 1, with different types of dementia, were included. The DDI study was designed to investigate early cognitive impairment and dementia markers. DDI participants included in this thesis were those classified as having Subjective cognitive decline (SCD) according to the SCD-I framework. Comprehensive clinical assessments, including measures of cognition, daily functioning and anthropometric measurement, blood samples, and brain MRI were performed in both studies. Brain morphology was studied using FreeSurfer segmentation and muscle morphology using slice O-Matic software.
Results
This thesis findings first indicate that nutritional status has an essential role in the 5-year prognosis of people living with dementia in the capacity to perform daily life activities and mortality. Second, the quality of the muscle, here the muscle of the tongue, and its amount of fat infiltration were associated with malnutrition onset in people with dementia. Finally, in patients with SCD, muscle function measured with the timed up and go test (TUG) was associated with cognitive decline. TUG, in addition, was associated with cortical thickness in areas related with cognitive and motor functioning.
Conclusion
Nutritional and muscular status predict prognosis in people with SCD and with dementia. These findings suggest that interventions focused on these areas may improve outcomes such as cognition, function, and survival in these groups
AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale
BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project
On harmonisation of brain MRI data across scanners and sites
Magnetic resonance imaging (MRI) of the brain has revolutionised neuroscience by opening unique opportunities for studying unknown aspects of brain organisation, function and pathology-induced dysfunction. Despite the huge potential, MRI measures can be limited in their consistency, reproducibility and accuracy which subsequently restricts their quantifiability. Nuisance non-biological factors, such as hardware, software, calibration differences between scanners and post-processing options can contribute or drive trends in neuroimaging features to an extent that interferes with biological variability and obstructs scientific explorations and clinical applications. Such lack of consistency, or harmonisation across neuroimaging datasets poses a great challenge for our capabilities in quantitative MRI. This thesis contributes to better understanding and addressing it. We specifically build a new resource for comprehensively mapping the extent of the problem and objectively evaluating neuroimaging harmonisation approaches. We use a travelling heads paradigm consisting of multimodal MRI data of 10 travelling subjects, each scanned at 5 different sites on 6 different 3.0T scanners from all the 3 major vendors and using 5 imaging modalities. We use this dataset to explore the between-scanner variability of hundreds of imaging-extracted features and compare these to within-scanner (within-subject) variability and biological (between-subject) variability. We identify subsets of features that are/are not reliable across scanners and use our resource as a testbed to enable new investigations which until now have been relatively unexplored. Specifically, we identify optimal pipeline processing steps that minimise between-scanner variability in extracted features (implicit harmonisation). We also test the performance of post-processing harmonisation tools (explicit harmonisation) and specifically check their efficiency in reducing between-scanner variability against baseline gold standards provided by our data. Our explorations allow us to come up with good practice suggestions on processing steps and sets of features where results are more consistent and reproducible and also set references for future studies in this field
Positron emission tomography and magnetic resonance imaging methods and datasets within the Dominantly Inherited Alzheimer Network (DIAN)
The Dominantly Inherited Alzheimer Network (DIAN) is an international collaboration studying autosomal dominant Alzheimer disease (ADAD). ADAD arises from mutations occurring in three genes. Offspring from ADAD families have a 50% chance of inheriting their familial mutation, so non-carrier siblings can be recruited for comparisons in case-control studies. The age of onset in ADAD is highly predictable within families, allowing researchers to estimate an individual\u27s point in the disease trajectory. These characteristics allow candidate AD biomarker measurements to be reliably mapped during the preclinical phase. Although ADAD represents a small proportion of AD cases, understanding neuroimaging-based changes that occur during the preclinical period may provide insight into early disease stages of \u27sporadic\u27 AD also. Additionally, this study provides rich data for research in healthy aging through inclusion of the non-carrier controls. Here we introduce the neuroimaging dataset collected and describe how this resource can be used by a range of researchers
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