Browsing by Author "Dottori, Martin"

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    The power of knowledge about dementia in Latin America across health professionals working on aging
    (2020) Ibanez, Agustin; Flichtentrei, Daniel; Hesse, Eugenia; Dottori, Martin; Tomio, Ailin; Slachevsky, Andrea; Serrano, Cecilia M; Gonzalez-Billaut, Christian; Custodio, Nilton; Miranda, Claudia; Bustin, Julian; Cetckovitch, Marcelo; Torrente, Fernando; Olavarria, Loreto; Leon, Tomas; Costa Beber, Barbara; Bruki, Sonia; Suemoto, Claudia K.; Nitrini, Ricardo; Miller, Bruce L.; Yokoyama, Jennifer S.
    Methods: We investigated opinions among health professionals working on aging in LACs (N =3365) with regression models including expertise-related information (public policies, BI), individual differences (work, age, academic degree), and location. Results: Experts specified low public policy knowledge (X2 = 41.27, P < .001), high levels of stigma (X2 = 2636.37, P < .001), almost absent BI knowledge (X2 = 56.58, P < .001), and needs for regional diagnostic manuals (X2 = 2893.63, df = 3, P < .001) and data-sharing platforms (X2 = 1267.5, df = 3, P < .001). Lack of dementia knowledge was modulated by different factors. An implemented BI-based treatment for a proposed prevention program improved perception across experts. Discussion: Our findings help to prioritize future potential actions of governmental agencies and non-governmental organizations (NGOs) to improve LACs’ dementia knowledge.
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    Towards affordable biomarkers of frontotemporal dementia: A classification study via network's information sharing
    (Nature Publishing Group, 2017) Dottori, Martin; Sedeño, Lucas; Martorell, Miguel; Alifano, Florencia; Hesse, Eugenia; Mikulan, Ezequiel; García, Adolfo; Ruiz-Tagle, Amparo; Lillo, Patricia; Slachevsky, Andrea; Serrano, Cecilia; Fraiman, Daniel; Ibañez, Agustin
    Developing effective and affordable biomarkers for dementias is critical given the difficulty to achieve early diagnosis. In this sense, electroencephalographic (EEG) methods offer promising alternatives due to their low cost, portability, and growing robustness. Here, we relied on EEG signals and a novel information-sharing method to study resting-state connectivity in patients with behavioral variant frontotemporal dementia (bvFTD) and controls. To evaluate the specificity of our results, we also tested Alzheimer’s disease (AD) patients. The classification power of the ensuing connectivity patterns was evaluated through a supervised classification algorithm (support vector machine). In addition, we compared the classification power yielded by (i) functional connectivity, (ii) relevant neuropsychological tests, and (iii) a combination of both. BvFTD patients exhibited a specific pattern of hypoconnectivity in mid-range frontotemporal links, which showed no alterations in AD patients. These functional connectivity alterations in bvFTD were replicated with a low-density EEG setting (20 electrodes). Moreover, while neuropsychological tests yielded acceptable discrimination between bvFTD and controls, the addition of connectivity results improved classification power. Finally, classification between bvFTD and AD patients was better when based on connectivity than on neuropsychological measures. Taken together, such findings underscore the relevance of EEG measures as potential biomarker signatures for clinical settings.

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