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In the brain, the TrkA receptor for Nerve Growth Factor (NGF) is expressed primarily in the cholinergic system. TrkA/NGF support neuronal health and function, and deficiencies in this axis are associated with progressive cholinergic neuron atrophy and death, and with cognitive deficit in disorders such as Down’s syndrome and Alzheimer’s disease. These observations led to the hypothesis that TrkA agonists may rescue atrophic cholinergic neurons and benefit cognition. Indeed, a small molecule TrkA partial agonist called D3 normalized TrkA signals and improved memory in cognitive impairment models of ageing and an APP mouse model of Alzheimer’s disease. Paradoxically, in young healthy mice chronic delivery of D3 caused impaired memory without impairing learning, a form of anterograde amnesia. Here, we use this as a model to study the mechanisms of impaired memory. In young healthy mice acute or chronic treatment with D3 induces hyperactivation of TrkA-mediated signals in hippocampus, and causes a deficit in hippocampal-dependent memory consolidation proximal to drug exposure, without affecting learning or memory retrieval. The impairment after acute drug exposure is reversible. The impairment after long-term drug exposure is irreversible, likely due to a decrease in hippocampal CA1 neuron basal arborization. These findings support the notion of a homeostatic role for TrkA in memory, and demonstrate the differential outcomes of TrkA (hyper)activation in healthy versus disease states.

Despite the important role of written language in everyday life, abnormalities in functional written communication have been sparsely investigated in Primary Progressive Aphasia (PPA). Prior studies have analyzed written language separately in the three variants of PPA - nonfluent (nfvPPA), logopenic (lvPPA), and semantic (svPPA) - but have rarely compared them to each other or to spoken language. Manual analysis of written language can be a time-consuming process. We developed a program which uses a language parser and quantifies content units (CU) and total units (U) in written language samples. The program was used to analyze written and spoken descriptions of the WAB Picnic scene, based on a pre-defined CU corpus. We then calculated the ratio of CU to U (CU/U Ratio) as a measure of content density. Our cohort included 115 participants (20 control participants for written, 20 control participants for spoken, 28 participants with nfvPPA, 30 with lvPPA, and 17 with svPPA). We compared written language between patients with PPA and control participants and written to spoken language in patients with the three variants of PPA. Finally, we analyzed CU and U in relation to the Progressive Aphasia Severity Scale Sum of Boxes and the Clinical Dementia Rating Sum of Boxes. Our program identified CU with a validity of 99.7% (95%CI 99.5 to 99.8) compared to manual annotation of the samples. All patients with PPA wrote fewer total units than controls (p<0.001). Patients with lvPPA (p=0.013) and svPPA (0.004) wrote fewer CU than controls. The CU/U Ratio was higher in nfvPPA and svPPA than controls (p=0.019 in both cases), but no different between lvPPA patients and controls (p=0.962). Participants with lvPPA (p<0.001) and svPPA (p=0.04) produced fewer CU in written samples compared to spoken. A two-way ANOVA showed all groups produced fewer units in written samples compared to spoken (p<0.001). However, the decrease in written CU compared to spoken was smaller than the decrease in written units compared to spoken in participants with PPA, resulting in a larger written CU/U Ratio when compared to spoken language (p<0.001). nfvPPA patients produced correlated written and spoken CU (R=0.5, p=0.009) and total units (R=0.64, p<0.001), but this was not the case for lvPPA or svPPA. Considering all PPA patients, fewer CU were produced in those with greater aphasia severity (PASS SoB, R=-0.24, p=0.04) and dementia severity (CDR SoB, R=-0.34, p=0.004). In conclusion, we observed reduced written content in patients with PPA compared to controls, with a preference for content over non-content units in patients with nfvPPA and svPPA. When comparing written to spoken language, we observed a similar "telegraphic" style in both modalities in patients with nfvPPA, which was different from patients with svPPA and lvPPA, who use significantly less non-content units in writing than in speech. Lastly, we show how our program provides a time-efficient tool, which could enable feedback and tracking of writing as an important feature of language and cognition.

Nonfluent aphasia is a language disorder characterized by simplified sentence structures as well as word-level abnormalities such as a reduced use of verbs and function words. According to the predominant account of the disorder, both structural and word-level features are caused by a core deficit in the processing of syntax. Under this account, however, it remains unclear why nonfluent patients choose semantically richer verbs and may have an intact comprehension of verbs and function words. Here, we propose and test the hypothesis that the word-level features of nonfluency reflect a process that selects lexically richer words to increase the information content of sentences. We use a computational linguistic method to measure the information content of sentences in the language of patients with nonfluent primary progressive aphasia (nfvPPA) (n = 36) and healthy controls (n = 133). We measure sentence information using surprisal, a metric calculated by the average probability of occurrence of words in a sentence given their preceding context. We found that by packaging their structurally simple sentences with lower frequency words, nfvPPA patients produce sentences with similar surprisal as that of healthy speakers. Furthermore, we found that higher sentence surprisal in nfvPPA correlates with a lower function-to-all-word ratio, a lower verb-to-noun ratio, and a higher heavy-to-all-verb ratio. Surprisal is an effective quantitative index of sentence information. Using surprisal allows for testing an account of nonfluent aphasia that regards word-level features of nonfluency as adaptive rather than defective symptoms, a finding that may entail revisions in therapeutic approaches to nonfluent speech.

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<b><i>Introduction:</i></b> Digital biomarkers may act as a tool for early detection of changes in cognition. It is important to understand public perception of technologies focused on monitoring cognition to better guide the design of these tools and inform patients appropriately about the associated risks and benefits. Health care systems may also play a role in the clinical, legal, and financial implications of such technologies. <b><i>Objective:</i></b> To evaluate public opinion on the use of passive technology for monitoring cognition. <b><i>Methods:</i></b> This was a one-time, Internet-based survey conducted in English and Spanish. <b><i>Results:</i></b> Within the English survey distributed in the USA (<i>n</i> = 173), 58.1% of respondents would be highly likely to agree to passive monitoring of cognition via a smartphone application. Thirty-eight percent of those with a higher degree of experience with technology were likely to agree to monitoring versus 20% of those with less experience with technology (<i>p</i> = 0.003). Sixty-two percent of non-health-care professionals were likely to agree to monitoring versus 45% of health-care workers (<i>p</i> = 0.012). There were significant concerns regarding privacy (<i>p</i> &#x3c; 0.01). We compared the surveys answered in Spanish in Costa Rica via logistic regression (<i>n</i> = 43, total <i>n</i> = 216), adjusting for age, education level, health-care profession, owning a smartphone, experience with technology, and perception of cognitive decline. Costa Rican/Spanish-speaking respondents were 7 times more likely to select a high probability of agreeing to such a technology (<i>p</i> &#x3c; 0.01). English-speaking respondents from the USA were 5 times more likely to be concerned about the impact on health insurance (<i>p</i> = 0.001) and life insurance (<i>p</i> = 0.01). <b><i>Conclusions:</i></b> Understanding public perception and ethical implications should guide the design of digital biomarkers for cognition. Privacy and the health-care system in which the participants take part are 2 major factors to be considered. It is the responsibility of researchers to convey the ethical and legal implications of cognition monitoring.

ObjectiveNonfluent aphasia is characterized by simplified sentence structures and word‐level abnormalities, including reduced use of verbs and function words. The predominant belief about the disease mechanism is that a core deficit in syntax processing causes both structural and word‐level abnormalities. Here, we propose an alternative view based on information theory to explain the symptoms of nonfluent aphasia. We hypothesize that the word‐level features of nonfluency constitute a distinct compensatory process to augment the information content of sentences to the level of healthy speakers. We refer to this process as lexical condensation.MethodsWe use a computational approach based on language models to measure sentence information through surprisal, a metric calculated by the average probability of occurrence of words in a sentence, given their preceding context. We apply this method to the language of patients with nonfluent primary progressive aphasia (nfvPPA; n = 36) and healthy controls (n = 133) as they describe a picture.ResultsWe found that nfvPPA patients produced sentences with the same sentence surprisal as healthy controls by using richer words in their structurally impoverished sentences. Furthermore, higher surprisal in nfvPPA sentences correlated with the canonical features of agrammatism: a lower function‐to‐all‐word ratio, a lower verb‐to‐noun ratio, a higher heavy‐to‐all‐verb ratio, and a higher ratio of verbs in ‐ing forms.InterpretationUsing surprisal enables testing an alternative account of nonfluent aphasia that regards its word‐level features as adaptive, rather than defective, symptoms, a finding that would call for revisions in the therapeutic approach to nonfluent language production. ANN NEUROL 2023