Intrusion errors during verbal fluency task in amyotrophic lateral sclerosis

Item-Level Analysis of Category Fluency Test Performance: A Systematic Review and Meta-Analysis of Studies of Normal and Neurologically Abnormal Ageing

While Category Fluency (CF) is widely used to help profile semantic memory, item-level scoring (ILS) approaches to this test have been proposed to obtain indices that are less influenced by non-semantic supportive functions. We systematically reviewed the literature to test the hypotheses that (1) compared with healthy adults, individuals with a clinical diagnosis suggestive of neurodegeneration generate words of lower semantic complexity; (2) compared with young adults, older adults generate words of higher semantic complexity. We searched six databases (date of search: 8 December 2023) for studies that relied on CF and ILS methods, in normal ageing and in age-associated neurodegeneration. Thirty-four studies were shortlisted: 27 on neurodegenerative conditions; 7 on normal ageing. Risk of bias was evaluated via a published checklist. Data were presented via qualitative synthesis. Most studies reported words of lower semantic complexity in relation to at least one item-level feature in individuals with mild cognitive impairment (MCI), Alzheimer's dementia (AD), and other neurodegenerative diseases. Post-hoc meta-analyses focussing on the MCI/AD continuum confirmed an effect on words' frequency (385 MCI/AD individuals and 350 controls; Hedges's G = 0.59) and age-of-acquisition (193 MCI/AD individuals and 161 controls; Hedges's G =  - 1.51). Studies on normal ageing, conversely, failed to demonstrate any overall effect. Most studies on MCI and AD have not relied on neurobiological diagnostic criteria. Moreover, only a small number of studies analysed ILS controlling for quantitative CF performance. Despite these two limitations, this study suggests that ILS can contribute to an in-depth characterisation of semantic memory in neurological ageing.

Verbal and nonverbal fluency in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a multi-system disorder that commonly affects cognition and behaviour. Verbal fluency impairments are consistently reported in ALS patients, and we aimed to investigate whether this deficit extends beyond the verbal domain. We further aimed to determine whether deficits are underpinned by a primary intrinsic response generation impairment (i.e., a global reduction across tasks), potentially related to apathy, or an inability to maintain responding over time (i.e., a 'drop off' pattern). Twenty-two ALS patients and 21 demographically-matched controls completed verbal and nonverbal fluency tasks (phonemic/semantic word fluency, design fluency, gesture fluency and ideational fluency), requiring the generation of responses over a specified time period. Fluency performance was analysed in terms of the overall number of novel items produced, as well as the number of items produced in the first 'initiation' and the remaining 'maintenance' time periods. ALS patients' overall performance was not globally reduced across tasks. Patients were impaired only on meaningful gesture fluency, which requires the generation of gestures that communicate meaning (e.g., waving). On phonemic fluency, ALS patients showed a 'drop off' pattern of performance, where they had difficulty maintaining responding over time, but this pattern was not evident on the other fluency tasks. Apathy did not appear to be related to fluency performance. The selective meaningful gesture fluency deficit, in the context of preserved meaningless gesture fluency, highlights that the retrieval of action knowledge may be weakened in early ALS.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme

BACKGROUND

Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.

METHOD

11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average. During the training sessions, the subjects performed a short pre-screening phase, followed by three variations of a novel visou-mental (VM) protocol. Features extracted from the pre-screening phase were used to construct predictive platforms using stepwise multivariate linear regression (MLR) models. In the test sessions, we employed a task-correction phase where our predictive models were used to predict the ideal task variation to maximize performance, followed by an interference-correction phase. We then investigated the associations between predicted and actual performances and evaluated the outcome of correction strategies.

RESULT

The predictive models resulted in respective adjusted R-squared values of 0.942, 0.724, and 0.939 for the first, second, and third variation of the task, respectively. The statistical analyses showed significant associations between the performances predicted by predictive models and the actual performances for the first two task variations, with rhos of 0.7289 (p-value = 0.011) and 0.6970 (p-value = 0.017), respectively. For 81.82 % of the subjects, the task/workload correction stage correctly determined which task variation provided the highest accuracy, with an average performance gain of 5.18 % when applying the correction strategies.

CONCLUSION

Our proposed method can lead to an integrated multimodal predictive framework to compensate for BCI performance variability, particularly, for people with severe motor deficits.

Post-Processing Automatic Transcriptions with Machine Learning for Verbal Fluency Scoring

Objective

To compare verbal fluency scores derived from manual transcriptions to those obtained using automatic speech recognition enhanced with machine learning classifiers.

Methods

Using Amazon Web Services, we automatically transcribed verbal fluency recordings from 1400 individuals who performed both animal and letter F verbal fluency tasks. We manually adjusted timings and contents of the automatic transcriptions to obtain "gold standard" transcriptions. To make automatic scoring possible, we trained machine learning classifiers to discern between valid and invalid utterances. We then calculated and compared verbal fluency scores from the manual and automatic transcriptions.

Results

For both animal and letter fluency tasks, we achieved good separation of valid versus invalid utterances. Verbal fluency scores calculated based on automatic transcriptions showed high correlation with those calculated after manual correction.

Conclusion

Many techniques for scoring verbal fluency word lists require accurate transcriptions with word timings. We show that machine learning methods can be applied to improve off-the-shelf ASR for this purpose. These automatically derived scores may be satisfactory for some applications. Low correlations among some of the scores indicate the need for improvement in automatic speech recognition before a fully automatic approach can be reliably implemented.

Cerebellar Functional Lateralization From the Perspective of Clinical Neuropsychology

Objective: The cerebellar functional laterality, with its right hemisphere predominantly involved in verbal performance and the left one engaged in visuospatial processes, has strong empirical support. However, the clinical observation and single research results show that the damage to the right cerebellar hemisphere may cause extralinguistic and more global cognitive decline. The aim of our research was to assess the pattern of cognitive functioning, depending on the cerebellar lesion side, with particular emphasis on the damage to the right cerebellar hemisphere.

Method: The study sample consisted of 31 patients with focal cerebellar lesions and 31 controls, free of organic brain damage. The Addenbrooke’s Cognitive Examination ACE III and the Trail Making Test TMT were used to assess patients’ cognitive functioning.

Results: Left-sided cerebellar lesion patients scored lower than controls in attention and visuospatial domain, but not in language, fluency, and memory functions. Participants with right-sided cerebellar lesion demonstrated a general deficit of cognitive functioning, with impairments not only in language and verbal fluency subscales but also in all ACE III domains, including memory, attention, and visuospatial functions. The TMT results proved that cerebellar damage is associated with executive function impairment, regardless of the lesion side.

Conclusion: The cognitive profiles of patients with cerebellum lesions differ with regard to the lesion side. Left-sided cerebellar lesions are associated with selective visuospatial and attention impairments, whereas the right-sided ones may result in a more global cognitive decline, which is likely secondary to language deficiencies, associated with this lateral cerebellar injury.