Alberto Benussi1, Mario Grassi2, Fernando Palluzzi2, Valentina Cantoni1, Maria Sofia Cotelli3, Enrico Premi1, Francesco Di Lorenzo4, Maria Concetta Pellicciari4, Federico Ranieri5, Gabriella Musumeci6, Camillo Marra7, Paolo Manganotti8, Raffaele Nardone9, Vincenzo Di Lazzaro6, Giacomo Koch10, Barbara Borroni11. 1. Neurology Unit, Department of Clinial and Experimental Sciences, University of Brescia, Italy. 2. Department of Brain and Behavioural Sciences, Medical and Genomic Statistics Unit, University of Pavia, Pavia, Italy. 3. Neurology Unit, Vallecamonica Hospital, Esine, Brescia, Italy. 4. Non Invasive Brain Stimulation Unit, IRCCS Santa Lucia Foundation, Rome, Italy. 5. Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona, Italy. 6. Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Campus Bio-Medico University, Rome, Italy. 7. Department of Neuroscience, Catholic University of Sacred Heart, Rome, Italy. 8. Neurology Unit, University of Trieste, Trieste, Italy. 9. Department of Neurology, Hospital of Merano (SABES-ASDAA), Merano-Meran, Italy; Department of Neurology, Christian Doppler Klinik, Paracelsus Medical University, Salzburg, Austria. 10. Non Invasive Brain Stimulation Unit, IRCCS Santa Lucia Foundation, Rome, Italy; Stroke Unit, Policlinico Tor Vergata, Rome, Italy. 11. Neurology Unit, Department of Clinial and Experimental Sciences, University of Brescia, Italy. Electronic address: bborroni@inwind.it.
Abstract
OBJECTIVE: To evaluate the performance of a Random Forest (RF) classifier on Transcranial Magnetic Stimulation (TMS) measures in patients with Mild Cognitive Impairment (MCI). METHODS: We applied a RF classifier on TMS measures obtained from a multicenter cohort of patients with MCI, including MCI-Alzheimer's Disease (MCI-AD), MCI-frontotemporal dementia (MCI-FTD), MCI-dementia with Lewy bodies (MCI-DLB), and healthy controls (HC). All patients underwent TMS assessment at recruitment (index test), with application of reference clinical criteria, to predict different neurodegenerative disorders. The primary outcome measures were the classification accuracy, precision, recall and F1-score of TMS in differentiating each disorder. RESULTS: 160 participants were included, namely 64 patients diagnosed as MCI-AD, 28 as MCI-FTD, 14 as MCI-DLB, and 47 as healthy controls (HC). A series of 3 binary classifiers was employed, and the prediction model exhibited high classification accuracy (ranging from 0.72 to 0.86), high precision (0.72-0.90), high recall (0.75-0.98), and high F1-scores (0.78-0.92), in differentiating each neurodegenerative disorder. By computing a new classifier, trained and validated on the current cohort of MCI patients, classification indices showed even higher accuracy (ranging from 0.83 to 0.93), precision (0.87-0.89), recall (0.83-1.00), and F1-scores (0.85-0.94). CONCLUSIONS: TMS may be considered a useful additional screening tool to be used in clinical practice in the prodromal stages of neurodegenerative dementias.
OBJECTIVE: To evaluate the performance of a Random Forest (RF) classifier on Transcranial Magnetic Stimulation (TMS) measures in patients with Mild Cognitive Impairment (MCI). METHODS: We applied a RF classifier on TMS measures obtained from a multicenter cohort of patients with MCI, including MCI-Alzheimer's Disease (MCI-AD), MCI-frontotemporal dementia (MCI-FTD), MCI-dementia with Lewy bodies (MCI-DLB), and healthy controls (HC). All patients underwent TMS assessment at recruitment (index test), with application of reference clinical criteria, to predict different neurodegenerative disorders. The primary outcome measures were the classification accuracy, precision, recall and F1-score of TMS in differentiating each disorder. RESULTS: 160 participants were included, namely 64 patients diagnosed as MCI-AD, 28 as MCI-FTD, 14 as MCI-DLB, and 47 as healthy controls (HC). A series of 3 binary classifiers was employed, and the prediction model exhibited high classification accuracy (ranging from 0.72 to 0.86), high precision (0.72-0.90), high recall (0.75-0.98), and high F1-scores (0.78-0.92), in differentiating each neurodegenerative disorder. By computing a new classifier, trained and validated on the current cohort of MCI patients, classification indices showed even higher accuracy (ranging from 0.83 to 0.93), precision (0.87-0.89), recall (0.83-1.00), and F1-scores (0.85-0.94). CONCLUSIONS: TMS may be considered a useful additional screening tool to be used in clinical practice in the prodromal stages of neurodegenerative dementias.
Authors: Andris Cerins; Daniel Corp; George Opie; Michael Do; Bridgette Speranza; Jason He; Pamela Barhoun; Ian Fuelscher; Peter Enticott; Christian Hyde Journal: Sci Rep Date: 2022-06-15 Impact factor: 4.996
Authors: Alberto Benussi; Valentina Cantoni; Jasmine Rivolta; Silvana Archetti; Anna Micheli; Nicholas Ashton; Henrik Zetterberg; Kaj Blennow; Barbara Borroni Journal: Alzheimers Res Ther Date: 2022-10-13 Impact factor: 8.823