Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data

Abstract

PurposeFrontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms.Materials & methodsBrain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, “leave one center out” conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis.ResultsGroup comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach.ConclusionOur study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.

Data-driven algorithm for the diagnosis of behavioral variant frontotemporal dementia

“…Previous studies classified bvFTD from a control group (12)(13)(14)(15)(16). The best AUC was reported by Raamana et al (AUC 0.938, 100% sensitivity and 88% specificity).…”

“…Lately, machine learning techniques have been applied to distinguish between bvFTD and Cognitively Normal Subjects (CNCs), Alzheimer Disease or other psychiatric and neurologic disorders on an individual level using MRI based features (12)(13)(14)(15)(16)(17)(18)(19)(20). Studies vary greatly on the subjects included and the methodology.…”

P5 eHealth: An Agenda for the Health Technologies of the Future

“…Frontotemporal dementia (FTD) is one of the most common causes of early onset dementia; among FTD profiles, behavioural FTD is the most frequent and is characterized by specific biomarker (Piguet et al 2011). Meyer and co-authors used MRI from multicentre cohort to predict diagnosis in each single patient showing the potential of precision medicine (Meyer et al 2017). They calculated brain atrophy differences between controls and patients and used SVM to differentiate these groups on an individual level.…”

L’intelligence artificielle à l’aide des myologues

“…Déjà en usage dans le cadre de recommandations thérapeutiques et de diagnostic, les techniques « d'apprentissage profond », outils qui permettent de transformer la masse énorme d'informations collectées en connaissance, aident des experts en imagerie médicale à mieux identifier, classer, quantifier, repérer les anomalies et interpréter des images issues de radiographies [12], de PET/Scan [13] et/ou d'IRM [14]. Ils favorisent le dépistage de certains cancers, de fractures et même d'atteintes dues à la maladie d'Alzheimer [15].…”