Dissociating Semantic and Phonemic Search Strategies in the Phonemic Verbal Fluency Task in early Dementia (2021)

Abstract: Effective management of dementia hinges on timely detection and precise diagnosis of the underlying cause of the syndrome at an early mild cognitive impairment (MCI) stage. Verbal fluency tasks are among the most often applied tests for early dementia detection due to their efficiency and ease of use. In these tasks, participants are asked to produce as many words as possible belonging to either a semantic category (SVF task) or a phonemic category (PVF task). Even though both SVF and PVF share neurocognitive function profiles, the PVF is typically believed to be less sensitive to measure MCI-related cognitive impairment and recent research on fine-grained automatic evaluation of VF tasks has mainly focused on the SVF. Contrary to this belief, we show that by applying state-of-the-art semantic and phonemic distance metrics in automatic analysis of PVF word productions, in-depth conclusions about production strategy of MCI patients are possible. Our results reveal a dissociation between semantically- and phonemically-guided search processes in the PVF. Specifically, we show that subjects with MCI rely less on semantic- and more on phonemic processes to guide their word production as compared to healthy controls (HC). We further show that semantic similarity-based features improve automatic MCI versus HC classification by 29% over previous approaches for the PVF. As such, these results point towards the yet underexplored utility of the PVF for in-depth assessment of cognition in MCI.

## introduction dementia is a syndrome primarily presenting with broad cognitive impairments. there are multiple underlying causes that result in dementia such as alzheimer's disease (ad) or fronto-temporal lobar degeneration or focal lesions @xcite . these sub-forms have different neurocognitive profiles. the most-common alzheimer's disease (ad)-related dementia is typically driven by an amnestic cognitive impairment @xcite whereas the fronto-temporal dementia is often associated with executive function impairment @xcite . early identification of dementia as well as precise differentiation between dementia sub-forms is crucial for effective management of the syndrome @xcite . pairing high diagnostic sensitivity with ease of use, verbal fluency tests (vf) are amongst the most-applied tests in cognitive assessment of dementia @xcite . in these tests, participants are asked to produce as many words from a specific category as they can in a fixed time. the two main variants of vf tests are the semantic verbal fluency (svf) and the phonemic verbal fluency (pvf). in the svf, the word category is defined by semantics (e.g. all animal words), whereas in the pvf participants need to produce words starting with a specific letter (e.g. "s"). traditionally, test scores are computed by counting the number of correctly named words within the given time @xcite . although both vf variants are quite similar in the way they engage different neurocognitive functions, the cognitive strategies of the task can indicate different patterns of the underlying neuropathology. for instance, an svf impairment is often only regarded as evidence for amnestic @xcite whereas a pvf impairment is almost exclusively regarded as evidence for fronto-temporal dementias @xcite . recently, advanced natural language processing (nlp) techniques have been applied to allow for in-depth analysis of the produced word sequence in vf tasks, particularly for the svf @xcite @xcite . by extracting clusters from the produced word sequence and by modelling the semantic relationships between-and within these clusters, it is possible to disentangle the effects of memory impairment from effects of executive function impairment @xcite . despite the success of these qualitative features in the svf, their utility for automatic analysis of the pvf remains underexplored. in this paper, we investigate both phonemic and semantic motivations for the underlying strategy of the phonemic verbal fluency task, and thereby reduce the gap between clinical theory and computational approaches to evaluating cognitive speech tasks. by contrasting semantic and phonemic distance measure in an analysis based on time bins, we show a dissociation between semantically-and phonemically-guided search processes: subjects with mild cognitive impairment (mci) exhibit significantly less semantic similarity in their productions as compared to healthy controls (hc). finally, in experiments on automatic classification of mci vs. hc from pvf word productions, we show that semantic features improve over previous approaches by 29%. taken together, our results pave the way towards more fine-grained analysis of the pvf task that can help to improve clinical decision processes. ## results results from the experiments to investigate strategic process as described in section 4.4.1 are visualized in figure 1 . significance testing between the hc and mci groups are given in ## discussion the phonemic verbal fluency task remains underexplored in its use for clinical assessment as well as research of mci. however, in this paper we show, that with stateof-the-art semantic as well as phonemic distance metrics, the pvf can reveal neurocognitive function involvement that is crucial to better assess mci. our data shows that with recent semantic and phonemic similarity metrics, we can capture mci-related impairments, such as a general semantic impairment, that have also been reported in the svf @xcite but not on the pvf. our results show significantly lower semantic distance for hc responses when compared to the mci group in the pvf task which is, by nature, phonemically motivated. in return, mci patients show significantly lower phonemic distance. this could possibly be explained by the mci group relying heavily on a phonemic strategy to guide their search rather than a utilizing a semantic strategy. the higher semantic distance for the mci group could be interpreted as a structural deficit to access semantic memory efficiently as has been shown to be very prominent at all stages of ad-related dementia @xcite . this is especially striking as one would expect by making neurocognitive processes visible in the pvf that are traditionally reserved for the svf in clinical practice, the pvf becomes significantly more relevant to real-world mci and dementia assessment. in order to support the diagnostic usage of the pvf for mci assessment, we simulate a diagnostic decision scenario through downstream machine learning classification using the semantic as well as phonemic features in the pvf. our results show that by using semantic and phonemic features we can improve classification results over previous clinical and automatic baselines. the all features model (@xmath2.71) out performs both the word count (@xmath3.50) and previous work of ryan (2013) (@xmath4.42). both clustering (@xmath5.64) and binning (@xmath6.67) methods of analysis perform comparatively. both the semantic (@xmath7.65) and phonemic (@xmath8.76) measures outperform the clinical baselines (0.50). the classification results support that while the task is overall a phonemic task, semantic investigation of the pvf is relevant for future research and capable of discriminating between hc and mci better than the clinical baseline. as an additional finding, the machine learning task benefits from a combined binning and clustering approach when modelling the phonemic processes (@xmath9.76), increasing over only phonemic clustering (@xmath10.45) or phonemic binning methods (@xmath11.70) for classification. ## conclusion this paper set out to investigate the ability of computational linguistic techniques for understanding phonemic and semantic cognitive processes of the under-explored phonemic verbal fluency task. utilizing three resolutions of analysis, temporal binning, clustering and global measures, combined with semantic and phonemic distance measures, we found semantic impairment in a phonemic task as has been hypothesized in previous clinical research. in addition to giving a finer-resolution for understanding the pvf task, the additional phonemic and semantic features improved classification over previous clinical and automatic baselines for early dementia detection with the pvf task. future work should investigate these measures in additional languages and possibly combine the features presented in this paper with medical imaging techniques to see if the findings can be replicated.