Complete Evaluation of Dementia: PET and MRI Correlation and Diagnosis for the Neuroradiologist

Association of the Area Deprivation Index With Dementia Basic Workup and Diagnosis in Central and Western Virginia: A Cross-Sectional Study

Background and Objectives

The Area Deprivation Index (ADI) provides a validated and multidimensional metric of areal disadvantage. Our goals were to determine if the ADI influences the likelihood of receiving workup based on published guidelines and an etiologic diagnosis of dementia in Central and Western Virginia.

Methods

We collected deidentified data from the electronic health record of individuals aged 50-105 years diagnosed with dementia at the University of Virginia (UVA) Medical Center (2016-2021) and at Carillion Clinic (2018-2021). Visit-specific ICD-10 codes were used to classify each dementia diagnosis as "disease-specific" (e.g., Alzheimer disease) or "general" (e.g., unspecified dementia). Following the American Academy of Neurology guidelines, we considered the evaluation performed as "adequate" if patients had vitamin B12, thyroid-stimulating hormone, and brain CT or magnetic resonance imaging within 6 months of the initial diagnosis. Census tract ADI was linked to study participants using the unique census tract identifier derived from the participants' home addresses at the time of diagnosis. Statistical modeling occurred under a Bayesian paradigm implemented using a standard code in R.

Results

The study included 13,431 individuals diagnosed with dementia at UVA (n = 7,152) and Carillion Clinic (n = 6,279). Of those, 32.5% and 20.4% received "disease-specific" diagnoses at UVA and Carillion Clinic and 8.2% and 20.4% underwent "adequate" workup, respectively. The adjusted relationship between census tract ADI and the likelihood of a disease-specific diagnosis was U-shaped: Residence in moderately disadvantaged areas was associated with the lowest likelihood of disease-specific diagnosis.

Discussion

Most patients diagnosed with dementia did not receive an adequate evaluation or an etiologic diagnosis. Those living in locations just above the national median ADI levels had the lowest likelihood of receiving an etiologic diagnosis, lower than those in the least and most deprived areas. Renewed awareness efforts among providers are needed to increase compliance with diagnostic guidelines.

Evaluation of Neurodegenerative Disorders with Amyloid-β, Tau, and Dopaminergic PET Imaging: Interpretation Pitfalls

Antiamyloid therapies for Alzheimer disease recently entered clinical practice, making imaging biomarkers for Alzheimer disease even more relevant to guiding patient management. Amyloid and tau PET are valuable tools that can provide objective evidence of Alzheimer pathophysiology in living patients and will increasingly be used to complement 18F-FDG PET in the diagnostic evaluation of cognitive impairment and dementia. Parkinsonian syndromes, also common causes of dementia, can likewise be evaluated with a PET imaging biomarker,18F-DOPA, allowing in vivo assessment of the presynaptic dopaminergic neurons. Understanding the role of these PET biomarkers will help the nuclear medicine physician contribute to the appropriate diagnosis and management of patients with cognitive impairment and dementia. To successfully evaluate brain PET examinations for neurodegenerative diseases, knowledge of the necessary protocol details for obtaining a reliable imaging study, inherent limitations for each PET radiopharmaceutical, and pitfalls in image interpretation is critical. This review will focus on underlying concepts for interpreting PET examinations, important procedural details, and guidance for avoiding potential interpretive pitfalls for amyloid, tau, and dopaminergic PET examinations.

The use of an anterior-posterior atrophy index to distinguish Alzheimer's disease from frontotemporal disorders: an automated volumetric MRI Study

BACKGROUND

Alzheimer's disease (AD) and frontotemporal dementia (FTD) require different treatments. Since clinical presentation can be nuanced, imaging biomarkers aid in diagnosis. Automated software such as Neuroreader (NR) provides volumetric imaging data, and indices between anterior and posterior brain areas have proven useful in distinguishing dementia subtypes in research cohorts. Existing indices are complex and require further validation in clinical settings.

PURPOSE

To provide initial validation for a simplified anterior-posterior index (API) from NR in distinguishing FTD and AD in a clinical cohort.

MATERIAL AND METHODS

A retrospective chart review was completed. We derived a simplified API: API = (logVA/VP-μ)/σ where V A is weighted volume of frontal and temporal lobes and V P of parietal and occipital lobes. μ and σ are the mean and standard deviation of logVA/VP computed for AD participants. Receiver operating characteristic (ROC) curves and regression analyses assessed the efficacy of the API versus brain areas in predicting diagnosis of AD versus FTD.

RESULTS

A total of 39 participants with FTD and 78 participants with AD were included. The API had an excellent performance in distinguishing AD from FTD with an area under the ROC curve of 0.82 and a positive association with diagnostic classification on logistic regression analysis (B = 1.491, P < 0.001).

CONCLUSION

The API successfully distinguished AD and FTD with excellent performance. The results provide preliminary validation of the API in a clinical setting.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

Hybrid PET/MRI with Flutemetamol and FDG in Alzheimer's Disease Clinical Continuum

AIMS

We aimed to investigate the interaction between β -amyloid (Aβ) accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes in the Alzheimer's disease (AD) clinical continuum.

BACKGROUND

Utility of positron emission tomography (PET) / magnetic resonance imaging (MRI) hybrid imaging for diagnostic categorization of the AD clinical continuum including subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI) and Alzheimer's disease dementia (ADD) has not been fully crystallized.

OBJECTIVE

To evaluate the interaction between Aβ accumulation and cerebral glucose metabolism, cerebral perfusion, and cerebral structural changes such as cortex thickness or cerebral white matter disease burden and to detect the discriminative yields of these imaging modalities in the AD clinical continuum.

METHODS

Fifty patients (20 women and 30 men; median age: 64 years) with clinical SCD (n=11), aMCI (n=17) and ADD (n=22) underwent PET/MRI with [18F]-fluoro-D-glucose (FDG) and [18F]- Flutemetamol in addition to cerebral blood flow (CBF) and quantitative structural imaging along with detailed cognitive assessment.

RESULTS

High Aβ deposition (increased temporal [18F]-Flutemetamol standardized uptake value ratio (SUVr) and centiloid score), low glucose metabolism (decreased temporal lobe and posterior cingulate [18F]-FDG SUVr), low parietal CBF and right hemispheric cortical thickness were independent predictors of low cognitive test performance.

CONCLUSION

Integrated use of structural, metabolic, molecular (Aβ) and perfusion (CBF) parameters contribute to the discrimination of SCD, aMCI, and ADD.

Interpretable brain disease classification and relevance-guided deep learning

Deep neural networks are increasingly used for neurological disease classification by MRI, but the networks' decisions are not easily interpretable by humans. Heat mapping by deep Taylor decomposition revealed that (potentially misleading) image features even outside of the brain tissue are crucial for the classifier's decision. We propose a regularization technique to train convolutional neural network (CNN) classifiers utilizing relevance-guided heat maps calculated online during training. The method was applied using T1-weighted MR images from 128 subjects with Alzheimer's disease (mean age = 71.9 ± 8.5 years) and 290 control subjects (mean age = 71.3 ± 6.4 years). The developed relevance-guided framework achieves higher classification accuracies than conventional CNNs but more importantly, it relies on less but more relevant and physiological plausible voxels within brain tissue. Additionally, preprocessing effects from skull stripping and registration are mitigated. With the interpretability of the decision mechanisms underlying CNNs, these results challenge the notion that unprocessed T1-weighted brain MR images in standard CNNs yield higher classification accuracy in Alzheimer's disease than solely atrophy.

Development of a deep learning network for Alzheimer’s disease classification with evaluation of imaging modality and longitudinal data

Objective. Neuroimaging uncovers important information about disease in the brain. Yet in Alzheimer’s disease (AD), there remains a clear clinical need for reliable tools to extract diagnoses from neuroimages. Significant work has been done to develop deep learning (DL) networks using neuroimaging for AD diagnosis. However, no particular model has emerged as optimal. Due to a lack of direct comparisons and evaluations on independent data, there is no consensus on which modality is best for diagnostic models or whether longitudinal information enhances performance. The purpose of this work was (1) to develop a generalizable DL model to distinguish neuroimaging scans of AD patients from controls and (2) to evaluate the influence of imaging modality and longitudinal data on performance. Approach. We trained a 2-class convolutional neural network (CNN) with and without a cascaded recurrent neural network (RNN). We used datasets of 772 (N AD = 364, N control = 408) 3D 18F-FDG PET scans and 780 (N AD = 280, N control = 500) T1-weighted volumetric-3D MR images (containing 131 and 144 patients with multiple timepoints) from the Alzheimer’s Disease Neuroimaging Initiative, plus an independent set of 104 (N AD = 63, N NC = 41) 18F-FDG PET scans (one per patient) for validation. Main Results. ROC analysis showed that PET-trained models outperformed MRI-trained, achieving maximum AUC with the CNN + RNN model of 0.93 ± 0.08, with accuracy 82.5 ± 8.9%. Adding longitudinal information offered significant improvement to performance on 18F-FDG PET, but not on T1-MRI. CNN model validation with an independent 18F-FDG PET dataset achieved AUC of 0.99. Layer-wise relevance propagation heatmaps added CNN interpretability. Significance. The development of a high-performing tool for AD diagnosis, with the direct evaluation of key influences, reveals the advantage of using 18F-FDG PET and longitudinal data over MRI and single timepoint analysis. This has significant implications for the potential of neuroimaging for future research on AD diagnosis and clinical management of suspected AD patients.

Post-Stroke Cognitive Impairment and Dementia

Poststroke cognitive impairment and dementia (PSCID) is a major source of morbidity and mortality after stroke worldwide. PSCID occurs as a consequence of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. Cognitive impairment and dementia manifesting after a clinical stroke is categorized as vascular even in people with comorbid neurodegenerative pathology, which is common in elderly individuals and can contribute to the clinical expression of PSCID. Manifestations of cerebral small vessel disease, such as covert brain infarcts, white matter lesions, microbleeds, and cortical microinfarcts, are also common in patients with stroke and likewise contribute to cognitive outcomes. Although studies of PSCID historically varied in the approach to timing and methods of diagnosis, most of them demonstrate that older age, lower educational status, socioeconomic disparities, premorbid cognitive or functional decline, life-course exposure to vascular risk factors, and a history of prior stroke increase risk of PSCID. Stroke characteristics, in particular stroke severity, lesion volume, lesion location, multiplicity and recurrence, also influence PSCID risk. Understanding the complex interaction between an acute stroke event and preexisting brain pathology remains a priority and will be critical for developing strategies for personalized prediction, prevention, targeted interventions, and rehabilitation. Current challenges in the field relate to a lack of harmonization of definition and classification of PSCID, timing of diagnosis, approaches to neurocognitive assessment, and duration of follow-up after stroke. However, evolving knowledge on pathophysiology, neuroimaging, and biomarkers offers potential for clinical applications and may inform clinical trials. Preventing stroke and PSCID remains a cornerstone of any strategy to achieve optimal brain health. We summarize recent developments in the field and discuss future directions closing with a call for action to systematically include cognitive outcome assessment into any clinical studies of poststroke outcome.

Cholinergic Receptor Modulation as a Target for Preventing Dementia in Parkinson's Disease

Parkinson’s disease (PD) is a neurodegenerative condition characterized by the loss of dopaminergic neurons in the substantia nigra pars compacta (SNpc) in the midbrain resulting in progressive impairment in cognitive and motor abilities. The physiological and molecular mechanisms triggering dopaminergic neuronal loss are not entirely defined. PD occurrence is associated with various genetic and environmental factors causing inflammation and mitochondrial dysfunction in the brain, leading to oxidative stress, proteinopathy, and reduced viability of dopaminergic neurons. Oxidative stress affects the conformation and function of ions, proteins, and lipids, provoking mitochondrial DNA (mtDNA) mutation and dysfunction. The disruption of protein homeostasis induces the aggregation of alpha-synuclein (α-SYN) and parkin and a deficit in proteasome degradation. Also, oxidative stress affects dopamine release by activating ATP-sensitive potassium channels. The cholinergic system is essential in modulating the striatal cells regulating cognitive and motor functions. Several muscarinic acetylcholine receptors (mAChR) and nicotinic acetylcholine receptors (nAChRs) are expressed in the striatum. The nAChRs signaling reduces neuroinflammation and facilitates neuronal survival, neurotransmitter release, and synaptic plasticity. Since there is a deficit in the nAChRs in PD, inhibiting nAChRs loss in the striatum may help prevent dopaminergic neurons loss in the striatum and its pathological consequences. The nAChRs can also stimulate other brain cells supporting cognitive and motor functions. This review discusses the cholinergic system as a therapeutic target of cotinine to prevent cognitive symptoms and transition to dementia in PD.