Cognitive and psychological characteristics in patients with Cerebral Amyloid Angiopathy: a literature review.

AIM: To review the current data on cognitive and psychological characteristics of patients with CAA and on the instruments used for their evaluation. METHODS: A systematic search was performed in Embase, Scopus and PubMed with terms related to "cerebral amyloid angiopathy", "neuropsychological measures" and "patient-reported outcome measures" from January 2001 to December 2021. RESULTS: Out of 2851 records, 18 articles were selected. The cognitive evaluation was present in all of which, while the psychological one only in five articles. The MMSE (Mini Mental State Examination), TMT (Trail Making Test), fluency test, verbal learning test, digit span, digit symbol and Rey figure tests were the most used cognitive tests, while executive function, memory, processing speed, visuospatial function, attention and language were the most frequent impaired cognitive functions. Depression was the most considered psychological factor usually measured with BDI (Beck Depression Inventory) and GDS (Geriatric Depression Scale). CONCLUSIONS: The results of this study might be used in clinical practice as a guide to choose cognitive and psychological instruments and integrate them in the clinical evaluation. The results might also be used in the research field for studies investigating the impact of cognitive and psychological variables on the disease course and for consensus studies aimed at define a standardized evaluation of these aspects.

Form to meaning mapping and the impact of explicit morpheme combination in novel word processing.

In the present study, we leveraged computational methods to explore the extent to which, relative to direct access to semantics from orthographic cues, the additional appreciation of morphological cues is advantageous while inducing the meaning of affixed pseudo-words. We re-analyzed data from a study on a lexical decision task for affixed pseudo-words. We considered a parsimonious model only including semantic variables (namely, semantic neighborhood density, entropy, magnitude, stem proximity) derived through a word-form-to-meaning approach (ngram-based). We then explored the extent to which the addition of equivalent semantic variables derived by combining semantic information from morphemes (combination-based) improved the fit of the statistical model explaining human data. Results suggest that semantic information can be extracted from arbitrary clusters of letters, yet a computational model of semantic access also including a combination-based strategy based on explicit morphological information better captures the cognitive mechanisms underlying human performance. This is particularly evident when participants recognize affixed pseudo-words as meaningful stimuli.