Abstract Details

Title Breaking Diagnostic Barriers: NODDI-Machine learning Fusion for FTD Differential Diagnosis

Topic Aging, Dementia, and Behavioral Neurology

Presentation(s) S1 - Alzheimer’s Disease and Related Dementias (2:12 PM-2:24 PM)

Poster/Presentation
Number 007

Background In this study, we identified differences in patterns of WM and GM alterations through the application of the NODDI diffusion model in different FTLD variants and developed a machine learning (ML) algorithm that accurately classified FTLD subtypes using diffusion MRI results and neuropsychological data.

Objective The investigation of in vivo microstructural white (WM) and grey-matter (GM) alterations through diffusion-MRI imaging in frontotemporal lobe degeneration (FTLD) patients holds potential in better understanding neuropathological changes.

Design/Methods Multi-shell diffusion sequences and a neuropsychological assessment were obtained in controls and FTLD patients: 35 behavioral-variant frontotemporal dementia (bvFTD), 20 semantic variant primary progressive aphasia (svPPA), 14 nonfluent variant primary progressive aphasia (nfvPPA), 9 semantic bvFTD (sbvFTD).

Results The analysis of diffusion metrics in all FTLD syndromes compared to controls, including fractional anisotropy (FA), intracellular volume fraction (ICVF), and orientation dispersion index (ODI), performed using tract-based (TBSS) and gray-matter based spatial statistic (GBSS), revealed specific patterns of WM and GM alterations for each variant. FA ahowed widespread WM alterations. ICVF showed reduction both in WM and GM (bilateral frontotemporal for bvFTD, left temporal-frontal for svPPA and nfvPPA, right temporal for sbvFTD). GM ODI reduction was present with a similar, but more diffuse pattern compared to ICVF. WM ODI alterations were also observed: (i) reduction in corpus callosum and corona radiata (bvFTD, svPPA, nfvPPA) and right corona radiata (sbvFTD) and (ii) increase in temporo-occipital WM bundles (bvFTD) and stria-terminalis (svPPA). A Support vector machine (SVM) algorithm trained on mean ICVF and ODI values from different lobes and a set on neuropsychological test achieved a 97.3% accuracy in classifying different clinical syndromes.

Conclusions This approach holds great potential for advancing our understanding of FTLD pathology, facilitating diagnosis, personalized management and treatment strategies at individual-level.

Authors/Disclosures
Edoardo G. Spinelli, MD PRESENTER	Dr. Spinelli has nothing to disclose.
Federica Agosta (San Raffaele Scientific Institute)	Federica Agosta has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Philips. Federica Agosta has received personal compensation in the range of $500-$4,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Elsevier INC.
Silvia Basaia	Silvia Basaia has nothing to disclose.
Stefano Pisano, MD	Dr. Pisano has nothing to disclose.
Elisa Canu (Ospedale San Raffaele)	The institution of Elisa Canu has received research support from Italian Ministry of Health .
Giordano Cecchetti (San Raffaele Hospital)	Giordano Cecchetti has nothing to disclose.
Alma Ghirelli (San Raffaele Research Institute)	Ms. Ghirelli has nothing to disclose.
Elisa Sibilla	No disclosure on file
Giuseppe Magnani	Giuseppe Magnani has nothing to disclose.
Francesca Caso, MD (Universita' Vita Salute San Raffaele)	Dr. Caso has nothing to disclose.
Paola Caroppo	No disclosure on file
Sara Prioni (Fondazione IRCCS Istituto Neurologico Carlo Besta)	No disclosure on file
Cristina Villa	No disclosure on file
Lucio Tremolizzo (UNIMIB)	No disclosure on file
Ildebrando H. Appollonio, MD (Neurology Section, Dept. of Medicine and Surgery, University of Milano Bicocca)	Dr. Appollonio has nothing to disclose.
Federico Verde	No disclosure on file
Nicola Ticozzi, MD (Istituto Auxologico Italiano)	The institution of Dr. Ticozzi has received research support from Italian Ministry of Health . The institution of Dr. Ticozzi has received research support from AriSLA.
Vincenzo Silani, MD, FAAN (University of Milan Medical School - IRCCS Istituto Auxologico Italiano)	Dr. Silani has nothing to disclose.
Massimo Filippi, MD, FAAN (Ospedale San Raffaele, Neuroimaging Research Unit)	Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Alexion, Almirall, Biogen, Merck, Novartis, Roche, Sanofi;. Dr. Filippi has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Alexion, Biogen, Bristol-Myers Squibb, Merck, Novartis, Roche, Sanofi, Sanofi-Aventis, Sanofi- Genzyme, Takeda. Dr. Filippi has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for Bayer, Biogen, Celgene, Chiesi Italia SpA, Eli Lilly, Genzyme, Janssen, Merck-Serono, Neopharmed Gentili, Novartis, Novo Nordisk, Roche, Sanofi, Takeda, and TEVA. Dr. Filippi has received personal compensation in the range of $5,000-$9,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Springer Nature. The institution of Dr. Filippi has received research support from Biogen Idec, Merck-Serono, Novartis, Roche, the Italian Ministry of Health, the Italian Ministry of University and Research, and Fondazione Italiana Sclerosi Multipla.