Reduced habit-driven errors in Parkinson’s Disease

Bannard, Colin; Leriche, Mariana; Bandmann, Oliver; Brown, Christopher H.; Ferracane, Elisa; Sánchez‐Ferro, Álvaro; Obeso, José A.; Redgrave, Peter; Stafford, Tom

doi:10.1038/s41598-019-39294-z

Cited by 14 publications

(15 citation statements)

References 22 publications

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“…One notable instance is the differential loss of dopamine neurotransmission from this region in Parkinson’s disease 7 a putative pathophysiological condition linked to the cost of life-long use of habits 105 . The problems Parkinson’s disease patients have with walking and writing 8, 106, 107 and the new learning of experimental habits 108, 109 , have been interpreted as an inability to express stimulus-response habits. In contrast, other neuropsychiatric complications such as addictions exhibit an excessive cue-dependent use of certain rewards linked to increased posterior putamen activity 10, 110 .…”

Section: Discussionmentioning

confidence: 99%

“…However, it is of great interest to understand how automatic habitual control operates in the normal circumstances of everyday life. It is also important to understand how brain disorders such as Parkinson’s disease 7, 8 , obsessive-compulsive disorder 9 , and drug addictions 10 lead to clinically dysfunctional patterns of habitual use of behaviour. Despite the importance of habitual control in daily life and clinical pathology, formal investigation of the inherent stimulus-response associations established in everyday life is in its infancy.…”

Section: Introductionmentioning

confidence: 99%

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An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits

Guida

Michiels

Redgrave

et al. 2021

Preprint

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The dorsolateral striatum plays a major role in stimulus-response habits that are learned in the experimental laboratory. Here, we use meta-analytic procedures to identify the neural circuits activated during the execution of stimulus-response behaviours acquired in everyday-life and those activated by habits acquired in the laboratory. In the case of everyday-life habits we dissociated motor and associative components. We found that motor-dominant stimulus-response associations developed outside the laboratory engaged posterior dorsal putamen, supplementary motor area (SMA) and cerebellum. Associative components were also represented in the posterior putamen. Meanwhile, newly learned habits relied more on the anterior putamen with activation expanding to caudate and nucleus accumbens. Importantly, common neural representations for both naturalistic and laboratory based habits were found in posterior left and anterior right putamen. Our findings suggest a common striatal substrate for behaviours with significant stimulus-response associations, independently of whether they were acquired in the laboratory or everyday-life.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits

Guida

Michiels

Redgrave

et al. 2021

Preprint

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“…In PD, depletion of dopamine in the basal ganglia causes the complex motor deficit of bradykinesia-reduced speed, reduced amplitude, loss of rhythm and loss of ability to sustain repetitive movement. Habitual actions such as speech and gait, which are associated with higher degrees of automaticity, are particularly affected [72,73]. It has been shown that over 80% of PD patients suffer from dysarthria [74,75].…”

Section: Discussionmentioning

confidence: 99%

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

et al. 2019

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In this paper, we have investigated the differences in the voices of Parkinson’s disease (PD) and age-matched control (CO) subjects when uttering three phonemes using two complexity measures: fractal dimension (FD) and normalised mutual information (NMI). Three sustained phonetic voice recordings, /a/, /u/ and /m/, from 22 CO (mean age = 66.91) and 24 PD (mean age = 71.83) participants were analysed. FD was first computed for PD and CO voice recordings, followed by the computation of NMI between the test groups: PD–CO, PD–PD and CO–CO. Four features reported in the literature—normalised pitch period entropy (Norm. PPE), glottal-to-noise excitation ratio (GNE), detrended fluctuation analysis (DFA) and glottal closing quotient (ClQ)—were also computed for comparison with the proposed complexity measures. The statistical significance of the features was tested using a one-way ANOVA test. Support vector machine (SVM) with a linear kernel was used to classify the test groups, using a leave-one-out validation method. The results showed that PD voice recordings had lower FD compared to CO (p < 0.008). It was also observed that the average NMI between CO voice recordings was significantly lower compared with the CO–PD and PD–PD groups (p < 0.036) for the three phonetic sounds. The average NMI and FD demonstrated higher accuracy (>80%) in differentiating the test groups compared with other speech feature-based classifications. This study has demonstrated that the voices of PD patients has reduced FD, and NMI between voice recordings of PD–CO and PD–PD is higher compared with CO–CO. This suggests that the use of NMI obtained from the sample voice, when paired with known groups of CO and PD, can be used to identify PD voices. These findings could have applications for population screening.

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“…Summary statistics are found in table 1. Bannard et al 2019). Patients were recruited to be in the early stages of PD (Hoehn-Yahr stages 0 − 2.5, UPDRS < 20 in the medicated state), with normal cognitive function and < 5 years from a confirmed diagnosis.…”

Section: Datasetsmentioning

confidence: 99%

“…Eleven patients and nine age-matched controls were tested during a visit to a hospital clinic in Spain (see Bannard et al 2019). The inclusion criteria for patients was the same as for the clinictested English sample.…”

Section: In-clinic Spanish Copy-typingmentioning

confidence: 99%

Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network

Dhir¹,

Edman²,

Ferro³

et al. 2020

Proceedings of the 24th Conference on Computational Natural Language Learning

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There is urgent need for non-intrusive tests that can detect early signs of Parkinson's disease (PD), a debilitating neurodegenerative disorder that affects motor control. Recent promising research has focused on disease markers evident in the fine-motor behaviour of typing. Most work to date has focused solely on the timing of keypresses without reference to the linguistic content. In this paper we argue that the identity of the key combinations being produced should impact how they are handled by people with PD, and provide evidence that natural language processing methods can thus be of help in identifying signs of disease. We test the performance of a bidirectional LSTM with convolutional features in distinguishing people with PD from agematched controls typing in English and Spanish, both in clinics and online. 1 (a) Classification: PwPD (b) Classification: Control

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Reduced habit-driven errors in Parkinson’s Disease

Cited by 14 publications

References 22 publications

An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits

An fMRI meta-analysis of the role of the striatum in everyday-life vs laboratory-developed habits

Complexity Measures of Voice Recordings as a Discriminative Tool for Parkinson’s Disease

Identifying robust markers of Parkinson’s disease in typing behaviour using a CNN-LSTM network

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