Abstract

Abstract Objective Socio-cultural factors obscure diagnostic specificity for neurodegenerative disorders, particularly among minority or foreign-born patients with limited access to education. Our objective with the following case presentation is to illustrate the utility of a Socially Responsible Neuropsychology (SRN) model in guiding clinical practice through the comprehensive incorporation of significant, yet common, socio-cultural factors throughout the neuropsychological assessment. Method The present case involved a 79-year-old Spanish-speaking Mexican woman with two years of education, who underwent two serial neuropsychological assessments, as well as, structural/functional neuroimaging to monitor acute onset of telegraphic speech following the kidnapping of her husband. The SRN model guided clinical decision-making in understanding the onset and progression of symptoms within the context of medical-psychiatric comorbidities intertwined with socio-cultural and educational factors. Results Mild vascular neurocognitive disorder and complicated grief were the initial diagnostic impressions as supported by structural neuroimaging. Neuropsychological assessment over time, however, revealed progressive global cognitive decline whereas psychiatric symptomatology remained stable. In the presence of equivocal converging evidence from available neuroimaging studies (PET vs MRI), strong suspicion for the language variant of frontotemporal neurocognitive disorder was considered given the onset and progressive decline of expressive/receptive language, executive and memory abilities, and qualitative evidence for progressive behavioral disihibition. Conclusion Telegraphic speech in an elderly Latina woman that initially appeared most consistent with vascular neurocognitive disorder in the context of complex trauma with possible conversion disorder, was deemed most consistent with a neurodegenerative process after careful consideration of the time course and evolution of the neurocognitive profile over time within an SRN model.

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