Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets

Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyse such scans could transform neuroimaging research. Yet, their potential remains untapped, since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artefacts, subject populations). Here we present SynthSeg+, an AI segmentation suite that enables, for the first time, robust analysis of heterogeneous clinical datasets. In addition to whole-brain segmentation, SynthSeg+ also performs cortical parcellation, intracranial volume estimation, and automated detection of faulty segmentations (mainly caused by scans of very low quality). We demonstrate SynthSeg+ in seven experiments, including an ageing study on 14,000 scans, where it accurately replicates atrophy patterns observed on data of much higher quality. SynthSeg+ is publicly released as a ready-to-use tool to unlock the potential of quantitative morphometry.Comment: under review, extension of MICCAI 2022 pape

Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Background Astrocytes and microglia react to Aβ plaques, neurofibrillary tangles, and neurodegeneration in the Alzheimer’s disease (AD) brain. Single-nuclei and single-cell RNA-seq have revealed multiple states or subpopulations of these glial cells but lack spatial information. We have developed a methodology of cyclic multiplex fluorescent immunohistochemistry on human postmortem brains and image analysis that enables a comprehensive morphological quantitative characterization of astrocytes and microglia in the context of their spatial relationships with plaques and tangles. Methods Single FFPE sections from the temporal association cortex of control and AD subjects were subjected to 8 cycles of multiplex fluorescent immunohistochemistry, including 7 astroglial, 6 microglial, 1 neuronal, Aβ, and phospho-tau markers. Our analysis pipeline consisted of: (1) image alignment across cycles; (2) background subtraction; (3) manual annotation of 5172 ALDH1L1+ astrocytic and 6226 IBA1+ microglial profiles; (4) local thresholding and segmentation of profiles; (5) machine learning on marker intensity data; and (6) deep learning on image features. Results Spectral clustering identified three phenotypes of astrocytes and microglia, which we termed “homeostatic,” “intermediate,” and “reactive.” Reactive and, to a lesser extent, intermediate astrocytes and microglia were closely associated with AD pathology (≤ 50 µm). Compared to homeostatic, reactive astrocytes contained substantially higher GFAP and YKL-40, modestly elevated vimentin and TSPO as well as EAAT1, and reduced GS. Intermediate astrocytes had markedly increased EAAT2, moderately increased GS, and intermediate GFAP and YKL-40 levels. Relative to homeostatic, reactive microglia showed increased expression of all markers (CD68, ferritin, MHC2, TMEM119, TSPO), whereas intermediate microglia exhibited increased ferritin and TMEM119 as well as intermediate CD68 levels. Machine learning models applied on either high-plex signal intensity data (gradient boosting machines) or directly on image features (convolutional neural networks) accurately discriminated control vs. AD diagnoses at the single-cell level. Conclusions Cyclic multiplex fluorescent immunohistochemistry combined with machine learning models holds promise to advance our understanding of the complexity and heterogeneity of glial responses as well as inform transcriptomics studies. Three distinct phenotypes emerged with our combination of markers, thus expanding the classic binary “homeostatic vs. reactive” classification to a third state, which could represent “transitional” or “resilient” glia.España Ministry of Science, Innovation, and Universities FPU fellowship to CM-CMassachusetts Alzheimer’s Disease Research Center grant P30AG062421 to BTH, and 1R56AG061196 to BTHAlzheimer’s Association (AACF17-524184 and AACF-17-524184-RAPID to AS-P

Causal inference in medical records and complementary systems pharmacology for metformin drug repurposing towards dementia.

Metformin, a diabetes drug with anti-aging cellular responses, has complex actions that may alter dementia onset. Mixed results are emerging from prior observational studies. To address this complexity, we deploy a causal inference approach accounting for the competing risk of death in emulated clinical trials using two distinct electronic health record systems. In intention-to-treat analyses, metformin use associates with lower hazard of all-cause mortality and lower cause-specific hazard of dementia onset, after accounting for prolonged survival, relative to sulfonylureas. In parallel systems pharmacology studies, the expression of two AD-related proteins, APOE and SPP1, was suppressed by pharmacologic concentrations of metformin in differentiated human neural cells, relative to a sulfonylurea. Together, our findings suggest that metformin might reduce the risk of dementia in diabetes patients through mechanisms beyond glycemic control, and that SPP1 is a candidate biomarker for metformin's action in the brain

R script for Altmetric Analysis

This script shows the process of going from publications to aggregations on the journal level. The calculation of delta is explained and performed using the data.table package, and graphics are generated with ggplot2. Computation was performed running R version 3.3.0 on OSX 10.10

result: Alzheimer's Publication data merged with Altmetric information

result is a dataset that contains mydata's publication information merged with a separate altmetric database containing the specific altmetric component values. It is used to generate density plots and track altmetric composition over time

Mathematical Formalism

This pdf explores the mathematical background of our median of medians method; walking through the theory and construction of delta and arriving at our method of aggregating multiple deltas

Ancillary Altmetric Datasets

These datasets are ancillary datasets used to abstract journal-wide information from the publication level. Dimensions-Publications is the raw data. result_altmetric_split provides a "melted" version (wide to long format) of the result dataset that allows us to see the raw difference in social aspects of altmetric vs. the other categories. Similarly, result_density is a melted version of result that allows us to do things like density estimation of the social aspects of altmetric vs. the other categories over time. <br

result: Alzheimer's Publication Data from 2012-2015

result is a list of all Alzheimer's Publications that belong to a journal that published in the Alzheimer's sphere at least 30 times in the 2012-2017 window.<br

Journal Data

This dataset includes journal specific information aggregated from the mydata dataset including the journal specific delta values for all 114 journals in consideration

Principal Components Analysis Results

res_pca_rotation is the rotation matrix that arose from a principal components analysis on our publication data from 2012-2017; it contains information about the correlation structure of each variable and each principal component. res_pca_summary contains information about the std. dev and cumulative variance for each component. This is the information we used to threshold our choice of components to 4