Diagnosis and Prognosis Using Machine Learning Trained on Brain Morphometry and White Matter Connectomes

Accurate, reliable prediction of risk for Alzheimer’s disease (AD) is essential for early, diseasemodifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of commonly available multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (study 1: Ilsan Dementia Cohort; N=211; 110 AD, 64 mild cognitive impairment [MCI], and 37 subjective memory complaints [SMC]) to test and validate the diagnostic models; and, secondly, Alzheimer’s Disease Neuroimaging Initiative (ADNI)-2 (study 2) to test the generalizability of the approach and the prognostic models with longitudinal follow up data. Our machine learning models trained on the morphometric and connectome estimates (number of features=34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) with iterative nested cross-validation in a single-site study, outperforming the benchmark model (FLAIR-based white matter hyperintensity volumes). In a generalizability study using ADNI-2, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) as CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). We also predicted MCI to AD progression with 69% accuracy, compared with the 70% accuracy using CSF biomarker model. The optimal classification accuracy in a single-site dataset and the reproduced results in multisite dataset show the feasibility of the high-throughput imaging analysis of multimodal MRI and data-driven machine learning for predictive modeling in AD

How should we be using biomarkers in trials of disease modification in Parkinson’s disease?

The recent validation of the alpha synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson’s disease has formed the backbone for a proposed staging system for incorporation in Parkinson’s disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson’s disease patients into trials (as distinct from patients with non- Parkinson’s disease parkinsonism or non- Parkinson’s disease tremors). There remain however further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson’s disease, namely: optimising the distinction between different alpha synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; as sensitive means of confirming target engagement; and in the early prediction of longer-term clinical benefit. For example; levels of cerebrospinal fluid proteins such as the lysosomal enzyme ß-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer disease like pathology (detectable through cerebrospinal fluid levels of Amyloid Beta-42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline of Parkinson’s disease even in its later stages. The exploitation of additional biomarkers to the alpha synuclein seed amplification assay will therefore greatly add to our ability to plan trials and assess disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson’s disease. However, correlation with clinical progression does not necessarily equate to causation and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson’s disease

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Integrating longitudinal information in hippocampal volume measurements for the early detection of Alzheimer's disease

Background: Structural MRI measures for monitoring Alzheimer's Disease (AD) progression are becoming instrumental in the clinical practice, and more so in the context of longitudinal studies. This investigation addresses the impact of four image analysis approaches on the longitudinal performance of the hippocampal volume. Methods: We present a hippocampal segmentation algorithm and validate it on a gold-standard manual tracing database. We segmented 460 subjects from ADNI, each subject having been scanned twice at baseline, 12-month and 24month follow-up scan (1.5T, T1 MRI). We used the bilateral hippocampal volume v and its variation, measured as the annualized volume change Λ=δv/year(mm3/y). Four processing approaches with different complexity are compared to maximize the longitudinal information, and they are tested for cohort discrimination ability. Reference cohorts are Controls vs. Alzheimer's Disease (CTRL/AD) and CTRL vs. Mild Cognitive Impairment who subsequently progressed to AD dementia (CTRL/MCI-co). We discuss the conditions on v and the added value of Λ in discriminating subjects. Results: The age-corrected bilateral annualized atrophy rate (%/year) were: -. 1.6 (0.6) for CTRL, -. 2.2 (1.0) for MCI-. nc, -. 3.2 (1.2) for MCI-. co and -. 4.0 (1.5) for AD. Combined (. v, Λ) discrimination ability gave an Area under the ROC curve (. auc). =. 0.93 for CTRL vs AD and auc=. 0.88 for CTRL vs MCI-. co. Conclusions: Longitudinal volume measurements can provide meaningful clinical insight and added value with respect to the baseline provided the analysis procedure embeds the longitudinal information

Computer aided diagnosis in temporal lobe epilepsy and Alzheimer's dementia

Computer aided diagnosis within neuroimaging must rely on advanced image processing techniques to detect and quantify subtle signal changes that may be surrogate indicators of disease state. This thesis proposes two such novel methodologies that are both based on large volumes of interest, are data driven, and use cross-sectional scans: appearance-based classification (ABC) and voxel-based classification (VBC).The concept of appearance in ABC represents the union of intensity and shape information extracted from magnetic resonance images (MRI). The classification method relies on a linear modeling of appearance features via principal components analysis, and comparison of the distribution of projection coordinates for the populations under study within a reference multidimensional appearance eigenspace. Classification is achieved using forward, stepwise linear discriminant analyses, in multiple cross-validated trials. In this work, the ABC methodology is shown to accurately lateralize the seizure focus in temporal lobe epilepsy (TLE), differentiate normal aging individuals from patients with either Alzheimer's dementia (AD) or Mild Cognitive Impairment (MCI), and finally predict the progression of MCI patients to AD. These applications demonstrated that the ABC technique is robust to different signal changes due to two distinct pathologies, to low resolution data and motion artifacts, and to possible differences inherent to multi-site acquisition.The VBC technique relies on voxel-based morphometry to identify regions of grey and white matter concentration differences between co-registered cohorts of individuals, and then on linear modeling of variables extracted from these regions. Classification is achieved using linear discriminant analyses within a multivariate space composed of voxel-based morphometry measures related to grey and white matter concentration, along with clinical variables of interest. VBC is shown to increase the accuracy of prediction of one-year clinical status from three to four out of five TLE patients having undergone selective amygdalo-hippocampectomy. These two techniques are shown to have the necessary potential to solve current problems in neurological research, assist clinical physicians with their decision-making process and influence positively patient management

DEVELOPMENT OF IMAGING MARKERS TO TRACK ALZHEIMER¿S DISEASE PROGRESSION IN HUMANS AND MOUSE MODELS

La Malattia di Alzheimer (AD) \ue8 la forma pi\uf9 comune di demenza nella popolazione anziana e affligge pi\uf9 35 milioni di persone nel mondo. Ad oggi, le uniche terapie approvate per la sua cura sono dirette a ridurre i sintomi. Lo sviluppo di nuovi farmaci \ue8 lungo e costoso. Il processo di scoperta \ue8 arduo in quanto i trial clinici coinvolgono un ampio campione di pazienti e implicano dei follow-up troppo lunghi. Inoltre il valore predittivo dei modelli sperimentali \ue8 limitato a causa della mancanza di marcatori omologhi nell\u2019uomo e nei modelli animali. Questo lavoro si inserisce in Pharmacog, un progetto europeo che vede la collaborazione di universit\ue0 ed industrie allo scopo di identificare biomarcatori affidabili e sensibili alla progressione di malattia in pazienti affetti da decadimento cognitivo lieve (MCI) e modelli animali di AD allo scopo di colmare il vuoto tra risultati clinici e preclinici. Nell\u2019uomo, i marcatori di neuroimmagine sono tra i pi\uf9 promettenti candidati nel tracciare la progressione di malattia. Innovazioni nelle tecniche di risonanza magnetica (MRI) rendono possibile l\u2019identificazione di marcatori omologhi nell\u2019uomo e nel topo. Prima dello studio di neuroimmagine nei pazienti MCI, \ue8 necessario verificare che eventuali cambiamenti individuati siano dovuti all\u2019effettiva progressione di malattia e non causati dalla variabilit\ue0 intra e tra i diversi scanner utilizzati nel progetto. Il primo scopo di questo lavoro \ue8 lo studio dei cambiamenti morfometrici e di diffusione in tre diversi modelli murini di Malattia Alzheimer (TASTPM, TauPS2APP e PDAPP da 3 a 22 mesi) tramite l\u2019utilizzo di tecniche MRI. A nove mesi abbiamo trovato una significativa riduzione rispetto ai controlli del volume del caudato-putamen e della corteccia frontale nei TASTPM e nei TauPS2APP (p< 0.001). L\u2019assottigliamento della corteccia entorinale era significativo alla stessa et\ue0 in tutte e tre i modelli (p< 0.001). Abbiamo inoltre individuato delle anormalit\ue0 dipendenti dall\u2019et\ue0 anche in diverse regione di sostanza bianca. Quelle pi\uf9 precoci erano nella commissura anteriore e nel corpo calloso dei TASTPM di 13 mesi (p< 0.001). I danni dei TASTPM sono associabili al pesante carico di amiloide ed alla marcata attivazione della glia e degli astrociti. Il secondo scopo dello studio \ue8 la valutazione e la comparazione della riproducibilit\ue0 di misure volumetriche e di spessore tra test e retest ottenute utilizzando due diversi metodi di processazione esistenti (Freesurfer sulla singola acquisizione o Freesurfer longitudinale). Inoltre abbiamo saggiato la riproducibilit\ue0 di un\u2019analisi per le immagini di diffusione messa a punto nel nostro laboratorio. A questo scopo ognuno degli otto centri europei coinvolti nel progetto e con diversi scanner a 3T ha arruolato un gruppo di 5 volontari sani e anziani sottoponendoli a 2 acquisizioni di risonanza ad almeno una settimana di distanza l\u2019una dall\u2019altra. Abbiamo trovato che la variabilit\ue0 intra e tra i diversi centri nei volumi estratti da queste acquisizioni era inferiore al 3% per le strutture pi\uf9 grandi (come il talamo) e minore del 6% per quelle pi\uf9 piccole (es. amigdala). La variabilit\ue0 degli spessori era meno del 6% e le variazioni dei parametri di diffusione erano prevalentemente nell\u2019intervallo del 2-3%. In conclusione, abbiamo identificato nei modelli analizzati dei marcatori di immagine sensibili alla progressione dell\u2019AD simili a quelli visti nell\u2019uomo e questo apre la strada al possibile utilizzo di una \u201cdistintiva collezione\u201d di marcatori murini di immagine nei trial clinici. I dati collezionati nella parte umana mostrano un pi\uf9 altra riproducibilit\ue0 dei risultati morfometrici ottenuti con l\u2019analisi longitudinale rispetto a quella sulla singola acquisizione (p< 0.01). Infine, abbiamo dimostrato che l\u2019analisi delle immagini di diffusione messa a punto nel nostro laboratorio d\ue0 risultati ugualmente riproducibili a quelli riportati in letteratura.Alzheimer\u2019s disease (AD) is the most common form of dementia in elderly population, affecting more than 35 million people worldwide. To date, the only approved therapies for AD focus on symptomatic relief. The development of new therapeutic agents is time consuming and costly. Drug discovery process is arduous because clinical trials are currently involving too wide sample of patients and long follow-up. Moreover, the predicting value of experimental models used nowadays is limited due to the lack of homologous markers in humans and animals. This work is a branch of Pharmacog, an industry-academic European project aimed at identifying reliable biomarkers that are sensitive to disease progression in patients with Mild Cognitive Impairment (MCI) and in AD animal models in order to bridge the gap between preclinical and clinical outcomes. Human neuroimaging markers are among the most promising candidates to track disease progression. In addition, advanced magnetic resonance imaging (MRI) allow the identification of homologous biomarkers in humans and mice. Prior to investigate neuroimaging biomarkers on MCI patients, we have to test that there is no significant effect of within and across MRI sites variability on brain AD-related longitudinal changes. The first aim of this work is the study of the morphometric and diffusion changes in three different AD mouse model (TASTPM, TauPS2APP and PDAPP from 3 to 22 months of age) through MRI. We found significant volume reduction starting at 9 months in the caudate-putamen and frontal cortex of TASTPM and TauPS2APP (p< 0.001) compared to non transgenic mice. The decrease in the enthorinal cortex thickness was significantly lower in all the strains (p< 0.001). We also found age-related diffusion abnormalities in different white matter regions of TASTPM. The earlier changes were found in the corpus callosum and anterior commissure of 13 months old mice (p< 0.001). In TASTPM, deficits detected with MRI are related to heavy amyloid pathology, marked gliosis and astrocitosys. The second aim of this study is the evaluation and comparison of test-retest reproducibility of brain volumes and thicknesses by two existing Freesurfer pipelines (longitudinal and cross-sectional). Moreover, we assessed the reliability of a diffusion pipeline developed in our lab. Eight different 3T MRI sites in Europe enrolled a group of 5 healthy elderly subjects scanned twice at least a week apart. We found that the within and across sites variability of volumes was less than 3% for larger brain structures (such as thalamus) and less than 6% for smaller regions (i.e., hippocampus). The thickness variability was less than 6% and diffusion indices variations were mostly within the range 2-3%. In conclusion, the present data identify imaging biomarkers of disease progression in mice similar to that seen in humans and pave the way of a murine \u201cimaging signature\u201d usefulness in clinical trials. Human data show significantly higher reproducibility of brain morphometry using the longitudinal pipeline than using the cross-sectional one (p< 0.01). Finally, we demonstrated that the reliability of the analysis of brain diffusion we implemented in our lab is comparable to data reported in the literature