Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model

Background: In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. Results: Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. Conclusions: TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

Performance of a [18F]Flortaucipir PET Visual Read Method Across the Alzheimer Disease Continuum and in Dementia With Lewy Bodies

Background and Objectives: Recently, the US Food and Drug Administration approved the tau-binding radiotracer [18F]flortaucipir and an accompanying visual read method to support the diagnostic process in cognitively impaired patients assessed for Alzheimer disease (AD). Studies evaluating this visual read method are limited. In this study, we evaluated the performance of the visual read method in participants along the AD continuum and dementia with Lewy bodies (DLB) by determining its reliability, accordance with semiquantitative analyses, and associations with clinically relevant variables. // Methods: We included participants who underwent tau-PET at Amsterdam University Medical Center. A subset underwent follow-up tau-PET. Two trained nuclear medicine physicians visually assessed all scans. Inter-reader agreement was calculated using Cohen κ. To examine the concordance of visual read tau positivity with semiquantification, we defined standardized uptake value ratio (SUVr) positivity using different threshold approaches. To evaluate the prognostic value of tau-PET visual read, we performed linear mixed models with longitudinal Mini-Mental State Examination (MMSE). // Results: We included 263 participants (mean age 68.5 years, 45.6% female), including 147 cognitively unimpaired (CU) participants, 97 amyloid-positive participants with mild cognitive impairment or AD dementia (AD), and 19 participants with DLB. The visual read inter-reader agreement was excellent (κ = 0.95, CI 0.91–0.99). None of the amyloid-negative CU participants (0/92 [0%]) and 1 amyloid-negative participant with DLB (1/12 [8.3%]) were tau-positive. Among amyloid-positive participants, 13 CU participants (13/52 [25.0%]), 85 with AD (85/97 [87.6%]), and 3 with DLB (3/7 [42.9%]) were tau-positive. Two-year follow-up visual read status was identical to baseline. Tau-PET visual read corresponded strongly to SUVr status, with up to 90.4% concordance. Visual read tau positivity was associated with a decline on the MMSE in CU participants (β = −0.52, CI −0.74 to −0.30, p < 0.001) and participants with AD (β = −0.30, CI −0.58 to −0.02, p = 0.04). // Discussion: The excellent inter-reader agreement, strong correspondence with SUVr, and longitudinal stability indicate that the visual read method is reliable and robust, supporting clinical application. Furthermore, visual read tau positivity was associated with prospective cognitive decline, highlighting its additional prognostic potential. Future studies in unselected cohorts are needed for a better generalizability to the clinical population. // Classification of Evidence: This study provides Class II evidence that [18F]flortaucipir visual read accurately distinguishes patients with low tau-tracer binding from those with high tau-tracer binding and is associated with amyloid positivity and cognitive decline. // Glossary: Aβ=β-amyloid; AD=Alzheimer disease; CU=cognitively unimpaired; DLB=dementia with Lewy bodies; US FDA=US Food and Drug Administration; GMM=Gaussian mixture model; LMM=linear mixed model; MCI=mild cognitive impairment; MMSE=Mini-Mental State Examination; OR=odds ratio; ROI=region of interest; SCD=subjective cognitive decline; SUVr=standardized uptake value ratio

Adopting transfer learning for neuroimaging: a comparative analysis with a custom 3D convolution neural network model.

BACKGROUND In recent years, neuroimaging with deep learning (DL) algorithms have made remarkable advances in the diagnosis of neurodegenerative disorders. However, applying DL in different medical domains is usually challenged by lack of labeled data. To address this challenge, transfer learning (TL) has been applied to use state-of-the-art convolution neural networks pre-trained on natural images. Yet, there are differences in characteristics between medical and natural images, also image classification and targeted medical diagnosis tasks. The purpose of this study is to investigate the performance of specialized and TL in the classification of neurodegenerative disorders using 3D volumes of 18F-FDG-PET brain scans. RESULTS Results show that TL models are suboptimal for classification of neurodegenerative disorders, especially when the objective is to separate more than two disorders. Additionally, specialized CNN model provides better interpretations of predicted diagnosis. CONCLUSIONS TL can indeed lead to superior performance on binary classification in timely and data efficient manner, yet for detecting more than a single disorder, TL models do not perform well. Additionally, custom 3D model performs comparably to TL models for binary classification, and interestingly perform better for diagnosis of multiple disorders. The results confirm the superiority of the custom 3D-CNN in providing better explainable model compared to TL adopted ones

The pedunculopontine nucleus is related to visual hallucinations in Parkinson’s disease: preliminary results of a voxel-based morphometry study

Visual hallucinations (VH) are common in Parkinson’s disease (PD) and lead to a poor quality of life. For a long time, dopaminergic therapy was considered to be the most important risk factor for the development of VH in PD. Recently, the cholinergic system, including the pedunculopontine nucleus (PPN), has been implicated in the pathophysiology of VH. The aim of the present study was to investigate grey matter density of the PPN region and one of its projection areas, the thalamus. Thirteen non-demented PD patients with VH were compared to 16 non-demented PD patients without VH, 13 demented PD patients (PDD) with VH and 11 patients with dementia with Lewy bodies (DLB). Isotropic 3-D T1-weighted MRI images (3T) were analysed using voxel-based morphometry (VBM) with the PPN region and thalamus as ROIs. PD and PDD patients with VH showed grey matter reductions of the PPN region and the thalamus compared to PD patients without VH. VH in PD(D) patients are associated with atrophy of the PPN region and its thalamic target area, suggesting that a cholinergic deficit may be involved in the development of VH in PD(D)

Synaptic Proteins Linked to HIV-1 Infection and Immunoproteasome Induction: Proteomic Analysis of Human Synaptosomes

Infection of the central nervous system with human immunodeficiency virus type 1 (HIV-1) can produce morphological changes in the neocortical synaptodendritic arbor that are correlated with neurocognitive impairment. To determine whether HIV-1 infection influences the protein composition of human synapses, a proteomic study of isolated nerve endings was undertaken. Synaptosomes from frontal neocortex were isolated using isopyknic centrifugation from 19 human brain specimens. Purity and enrichment were assessed by measuring pre- and postsynaptic protein markers. Two-dimensional polyacrylamide gel electrophoresis and matrix-assisted laser desorption ionization time-of-flight mass spectrometry was used to screen for proteins differentially expressed in HIV/AIDS. The concentrations of 31 candidate protein spots were potentially abnormal in HIV-infected decedents with HIV encephalitis and/or increased expression of immunoproteasome subunits. Immunoblots showed that the concentration of some of them was related to HIV-1 infection of the brain and immunoproteasome (IPS) induction. Synapsin 1b and stathmin were inversely related to brain HIV-1 load; 14-3-3ζ and 14-4-4ε proteins were higher in subjects with HIV-1 loads. Perturbed synaptosome proteins were linked with IPS subunit composition, and 14-3-3ζ was histologically colocalized with IPS subunits in stained neocortical neurons. Proteomics illustrates that certain human proteins within the synaptic compartment are involved with changes in the synaptodendritic arbor and neurocognitive impairment in HIV-1-infected people

Exome sequencing identifies novel AD-associated genes

The genetic component of Alzheimer’s disease (AD) has been mainly assessed using Genome Wide Association Studies (GWAS), which do not capture the risk contributed by rare variants. Here, we compared the gene-based burden of rare damaging variants in exome sequencing data from 32,558 individuals —16,036 AD cases and 16,522 controls— in a two-stage analysis. Next to known genes TREM2, SORL1 and ABCA7, we observed a significant association of rare, predicted damaging variants in ATP8B4 and ABCA1 with AD risk, and a suggestive signal in ADAM10. Next to these genes, the rare variant burden in RIN3, CLU, ZCWPW1 and ACE highlighted these genes as potential driver genes in AD-GWAS loci. Rare damaging variants in these genes, and in particular loss-of-function variants, have a large effect on AD-risk, and they are enriched in early onset AD cases. The newly identified AD-associated genes provide additional evidence for a major role for APP-processing, Aβ-aggregation, lipid metabolism and microglial function in AD

Analysis of C9orf72 repeat expansions in a large international cohort of dementia with Lewy bodies

C9orf72 repeat expansions are a common cause of amyotrophic lateral sclerosis and frontotemporal dementia. To date, no large-scale study of dementia with Lewy bodies (DLB) has been undertaken to assess the role of C9orf72 repeat expansions in the disease. Here, we investigated the prevalence of C9orf72 repeat expansions in a large cohort of DLB cases and identified no pathogenic repeat expansions in neuropathologically or clinically defined cases, showing that C9orf72 repeat expansions are not causally associated with DLB. (C) 2016 Elsevier Inc. All rights reserved.Peer reviewe