Interpreting 123I-ioflupane dopamine transporter scans using hybrid scores

Personalized whole-brain neural mass models reveal combined Aβ and tau hyperexcitable influences in Alzheimer's disease

Neuronal dysfunction and cognitive deterioration in Alzheimer's disease (AD) are likely caused by multiple pathophysiological factors. However, mechanistic evidence in humans remains scarce, requiring improved non-invasive techniques and integrative models. We introduce personalized AD computational models built on whole-brain Wilson-Cowan oscillators and incorporating resting-state functional MRI, amyloid-β (Aβ) and tau-PET from 132 individuals in the AD spectrum to evaluate the direct impact of toxic protein deposition on neuronal activity. This subject-specific approach uncovers key patho-mechanistic interactions, including synergistic Aβ and tau effects on cognitive impairment and neuronal excitability increases with disease progression. The data-derived neuronal excitability values strongly predict clinically relevant AD plasma biomarker concentrations (p-tau217, p-tau231, p-tau181, GFAP) and grey matter atrophy obtained through voxel-based morphometry. Furthermore, reconstructed EEG proxy quantities show the hallmark AD electrophysiological alterations (theta band activity enhancement and alpha reductions) which occur with Aβ-positivity and after limbic tau involvement. Microglial activation influences on neuronal activity are less definitive, potentially due to neuroimaging limitations in mapping neuroprotective vs detrimental activation phenotypes. Mechanistic brain activity models can further clarify intricate neurodegenerative processes and accelerate preventive/treatment interventions.

A how-to guide for a precision medicine approach to the diagnosis and treatment of Alzheimer's disease

Article purpose

The clinical approach to Alzheimer's disease (AD) is challenging, particularly in high-functioning individuals. Accurate diagnosis is crucial, especially given the significant side effects, including brain hemorrhage, of newer monoclonal antibodies approved for treating earlier stages of Alzheimer's. Although early treatment is more effective, early diagnosis is also more difficult. Several clinical mimickers of AD exist either separately, or in conjunction with AD pathology, adding to the diagnostic complexity. To illustrate the clinical decision-making process, this study includes de-identified cases and reviews of the underlying etiology and pathology of Alzheimer's and available therapies to exemplify diagnostic and treatment subtleties.

Problem

The clinical presentation of Alzheimer's is complex and varied. Multiple other primary brain pathologies present with clinical phenotypes that can be difficult to distinguish from AD. Furthermore, Alzheimer's rarely exists in isolation, as almost all patients also show evidence of other primary brain pathologies, including Lewy body disease and argyrophilic grain disease. The phenotype and progression of AD can vary based on the brain regions affected by pathology, the coexistence and severity of other brain pathologies, the presence and severity of systemic comorbidities such as cardiac disease, the common co-occurrence with psychiatric diagnoses, and genetic risk factors. Additionally, symptoms and progression are influenced by an individual's brain reserve and cognitive reserve, as well as the timing of the diagnosis, which depends on the demographics of both the patient and the diagnosing physician, as well as the availability of biomarkers.

Methods

The optimal clinical and biomarker strategy for accurately diagnosing AD, common neuropathologic co-morbidities and mimickers, and available medication and non-medication-based treatments are discussed. Real-life examples of cognitive loss illustrate the diagnostic and treatment decision-making process as well as illustrative treatment responses.

Implications

AD is best considered a syndromic disorder, influenced by a multitude of patient and environmental characteristics. Additionally, AD existing alone is a unicorn, as there are nearly always coexisting other brain pathologies. Accurate diagnosis with biomarkers is essential. Treatment response is affected by the variables involved, and the effective treatment of Alzheimer's disease, as well as its prevention, requires an individualized, precision medicine strategy.

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease

MRI was suggested as a promising method for the diagnosis and assessment of Parkinson's Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T₂* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T₂* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T₂* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can't be performed. Combining neuromelanin and T₂*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

Evaluating dopamine transporter imaging as an enrichment biomarker in a phase 2 Parkinson's disease trial

BACKGROUND

Dopamine transporter single-photon emission computed tomography (DaT-SPECT) can quantify the functional integrity of the dopaminergic nerve terminals and has been suggested as an imaging modality to verify the clinical diagnosis of Parkinson's disease (PD). Depending on the stage of progression, approximately 5-15% of participants clinically diagnosed with idiopathic PD have been observed in previous studies to have normal DaT-SPECT patterns. However, the utility of DaT-SPECT in enhancing early PD participant selection in a global, multicenter clinical trial of a potentially disease-modifying therapy is not well understood.

METHODS

The SPARK clinical trial was a phase 2 trial of cinpanemab, a monoclonal antibody against alpha-synuclein, in participants with early PD. DaT-SPECT was performed at screening to select participants with DaT-SPECT patterns consistent with degenerative parkinsonism. Acquisition was harmonised across 82 sites. Images were reconstructed and qualitatively read at a central laboratory by blinded neuroradiologists for inclusion prior to automated quantitative analysis.

RESULTS

In total, 482 unique participants were screened between January 2018 and May 2019; 3.8% (15/398) of imaged participants were excluded owing to negative DaT-SPECT findings (i.e., scans without evidence of dopaminergic deficit [SWEDD]).

CONCLUSION

A smaller proportion of SPARK participants were excluded owing to SWEDD status upon DaT-SPECT screening than has been reported in prior studies. Further research is needed to understand the reasons for the low SWEDD rate in this study and whether these results are generalisable to future studies. If supported, the radiation risks, imaging costs, and operational burden of DaT-SPECT for enrichment may be mitigated by clinical assessment and other study design aspects.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03318523 . Date submitted: October 19, 2017. First Posted: October 24, 2017.