FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep behavior disorder

Subspace corrected relevance learning with application in neuroimaging

In machine learning, data often comes from different sources, but combining them can introduce extraneous variation that affects both generalization and interpretability. For example, we investigate the classification of neurodegenerative diseases using FDG-PET data collected from multiple neuroimaging centers. However, data collected at different centers introduces unwanted variation due to differences in scanners, scanning protocols, and processing methods. To address this issue, we propose a two-step approach to limit the influence of center-dependent variation on the classification of healthy controls and early vs. late-stage Parkinson's disease patients. First, we train a Generalized Matrix Learning Vector Quantization (GMLVQ) model on healthy control data to identify a "relevance space" that distinguishes between centers. Second, we use this space to construct a correction matrix that restricts a second GMLVQ system's training on the diagnostic problem. We evaluate the effectiveness of this approach on the real-world multi-center datasets and simulated artificial dataset. Our results demonstrate that the approach produces machine learning systems with reduced bias - being more specific due to eliminating information related to center differences during the training process - and more informative relevance profiles that can be interpreted by medical experts. This method can be adapted to similar problems outside the neuroimaging domain, as long as an appropriate "relevance space" can be identified to construct the correction matrix.

99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder

BACKGROUND AND OBJECTIVES

Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD.

METHODS

Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease.

RESULTS

Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features.

DISCUSSION

This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.

Deep learning based diagnosis of Alzheimer's disease using FDG-PET images

PURPOSE

The aim of this study is to develop a deep neural network to diagnosis Alzheimer's disease and categorize the stages of the disease using FDG-PET scans. Fluorodeoxyglucose positron emission tomography (FDG-PET) is a highly effective diagnostic tool that accurately detects glucose metabolism in the brain of AD patients.

MATERIAL AND METHODS

In this work, we have developed a deep neural network using FDG-PET to discriminate Alzheimer's disease subjects from stable mild cognitive impairment (sMCI), progressive mild cognitive impairment (pMCI), and cognitively normal (CN) cohorts. A total of 83 FDG-PET scans are collected from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including 21 subjects with CN, 21 subjects with sMCI, 21 subjects with pMCI, and 20 subjects with AD.

RESULTS

The method has achieved remarkable accuracy rates of 99.31% for CN vs. AD, 99.88% for CN vs. MCI, 99.54% for AD vs. MCI, and 96.81% for pMCI vs. sMCI. Based on the experimental results.

CONCLUSION

The results show that the proposed method has a significant generalisation ability as well as good performance in predicting the conversion of MCI to AD even in the absence of direct information. FDG-PET is a well-known biomarker for the identification of Alzheimer's disease using transfer learning.

Comparison of univariate and multivariate analyses for brain [18F]FDG PET data in α-synucleinopathies

BACKGROUND

Brain imaging with [18F]FDG-PET can support the diagnostic work-up of patients with α-synucleinopathies. Validated data analysis approaches are necessary to evaluate disease-specific brain metabolism patterns in neurodegenerative disorders. This study compared the univariate Statistical Parametric Mapping (SPM) single-subject procedure and the multivariate Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) in a cohort of patients with α-synucleinopathies.

METHODS

We included [18F]FDG-PET scans of 122 subjects within the α-synucleinopathy spectrum: Parkinson's Disease (PD) normal cognition on long-term follow-up (PD - low risk to dementia (LDR); n = 28), PD who developed dementia on clinical follow-up (PD - high risk of dementia (HDR); n = 16), Dementia with Lewy Bodies (DLB; n = 67), and Multiple System Atrophy (MSA; n = 11). We also included [18F]FDG-PET scans of isolated REM sleep behaviour disorder (iRBD; n = 51) subjects with a high risk of developing a manifest α-synucleinopathy. Each [18F]FDG-PET scan was compared with 112 healthy controls using SPM procedures. In the SSM/PCA approach, we computed the individual scores of previously identified patterns for PD, DLB, and MSA: PD-related patterns (PDRP), DLBRP, and MSARP. We used ROC curves to compare the diagnostic performances of SPM t-maps (visual rating) and SSM/PCA individual pattern scores in identifying each clinical condition across the spectrum. Specifically, we used the clinical diagnoses ("gold standard") as our reference in ROC curves to evaluate the accuracy of the two methods. Experts in movement disorders and dementia made all the diagnoses according to the current clinical criteria of each disease (PD, DLB and MSA).

RESULTS

The visual rating of SPM t-maps showed higher performance (AUC: 0.995, specificity: 0.989, sensitivity 1.000) than PDRP z-scores (AUC: 0.818, specificity: 0.734, sensitivity 1.000) in differentiating PD-LDR from other α-synucleinopathies (PD-HDR, DLB and MSA). This result was mainly driven by the ability of SPM t-maps to reveal the limited or absent brain hypometabolism characteristics of PD-LDR. Both SPM t-maps visual rating and SSM/PCA z-scores showed high performance in identifying DLB (DLBRP = AUC: 0.909, specificity: 0.873, sensitivity 0.866; SPM t-maps = AUC: 0.892, specificity: 0.872, sensitivity 0.910) and MSA (MSARP: AUC: 0.921, specificity: 0.811, sensitivity 1.000; SPM t-maps: AUC: 1.000, specificity: 1.000, sensitivity 1.000) from other α-synucleinopathies. PD-HDR and DLB were comparable for the brain hypo and hypermetabolism patterns, thus not allowing differentiation by SPM t-maps or SSM/PCA. Of note, we found a gradual increase of PDRP and DLBRP expression in the continuum from iRBD to PD-HDR and DLB, where the DLB patients had the highest scores. SSM/PCA could differentiate iRBD from DLB, reflecting specifically the differences in disease staging and severity (AUC: 0.938, specificity: 0.821, sensitivity 0.941).

CONCLUSIONS

SPM-single subject maps and SSM/PCA are both valid methods in supporting diagnosis within the α-synucleinopathy spectrum, with different strengths and pitfalls. The former reveals dysfunctional brain topographies at the individual level with high accuracy for all the specific subtype patterns, and particularly also the normal maps; the latter provides a reliable quantification, independent from the rater experience, particularly in tracking the disease severity and staging. Thus, our findings suggest that differences in data analysis approaches exist and should be considered in clinical settings. However, combining both methods might offer the best diagnostic performance.

Syndrome Pattern Recognition Method Using Sensed Patient Data for Neurodegenerative Disease Progression Identification

Neurodegenerative diseases are a group of conditions that involve the progressive loss of function of neurons in the brain and spinal cord. These conditions can result in a wide range of symptoms, such as difficulty with movement, speech, and cognition. The causes of neurodegenerative diseases are poorly understood, but many factors are believed to contribute to the development of these conditions. The most important risk factors include ageing, genetics, abnormal medical conditions, toxins, and environmental exposures. A slow decline in visible cognitive functions characterises the progression of these diseases. If left unattended or unnoticed, disease progression can result in serious issues such as the cessation of motor function or even paralysis. Therefore, early recognition of neurodegenerative diseases is becoming increasingly important in modern healthcare. Many sophisticated artificial intelligence technologies are incorporated into modern healthcare systems for the early recognition of these diseases. This research article introduces a Syndrome-dependent Pattern Recognition Method for the early detection and progression monitoring of neurodegenerative diseases. The proposed method determines the variance between normal and abnormal intrinsic neural connectivity data. The observed data is combined with previous and healthy function examination data to identify the variance. In this combined analysis, deep recurrent learning is exploited by tuning the analysis layer based on variance suppressed by identifying normal and abnormal patterns in the combined analysis. This variance from different patterns is recurrently used to train the learning model for maximising of recognition accuracy. The proposed method achieves 16.77% high accuracy, 10.55% high precision, and 7.69% high pattern verification. It reduces the variance and verification time by 12.08% and 12.02%, respectively.