Finger-tapping Task Research Articles

Age-related differences in finger interdependence during complex hand movements.

The well-known decrease in finger dexterity during healthy aging leads to a significant reduction in quality of life. Still, the exact patterns of altered finger kinematics of older adults in daily life are fairly unexplored. Finger interdependence is the unintentional comovement of fingers that are not intended to move, and it is known to vary across the lifespan. Nevertheless, the magnitude and direction of age-related differences in finger interdependence are ambiguous across studies and tasks and have not been explored in the context of daily life finger movements. We investigated five different free and daily-life-inspired finger movements of the right, dominant hand as well as a sequential finger tapping task of the thumb against the other fingers, in 17 younger (22-37 yr) and 17 older (62-80 yr) adults using an exoskeleton data glove for data recording. Using inferential statistics, we found that the unintentional comovement of fingers generally decreases with age in all performed daily-life-inspired movements. Finger tapping, however, showed a trend towards higher finger interdependence for older compared with younger adults. Using machine learning, we predicted the age group of a person from finger interdependence features of single movement trials significantly better than chance level for the daily-life-inspired movements, but not for finger tapping. Taken together, we show that for specific tasks, decreased finger interdependence (i.e., less comovement) could potentially act as a marker of human aging that specifically characterizes older adults' complex finger movements in daily life.NEW & NOTEWORTHY Kinematic finger movement data were analyzed with regard to age-related differences. Extensive analyses of complex and daily-life-inspired movements reveal that the direction of age effects is not uniform but task-dependent: Although older adults generally show more finger interdependence than younger adults in a simple finger tapping task, this effect is reversed for daily-life-inspired movement tasks. For these tasks, finger interdependence indices offer potential new markers to predict the age group of an individual using machine learning approaches.

An anatomo-functional study of the interactivity between the paracentral lobule and the primary motor cortex.

The purpose of this study was to understand the anatomical and functional connections between the paracentral lobule (PCL) and the primary motor cortex (M1) of the human brain. This retrospective study included 16 patients who underwent resection of lesions located near M1. Nine patients had lesions in the dominant hemisphere. Tractography was performed to visualize the connectivity between two regions of interest (ROIs)-the convexity and the interhemispheric fissure-that were shown by functional MRI to be activated during a finger tapping task. The number, mean length, and fractional anisotropy (FA) of the fibers between the ROIs were estimated. During surgery, subdural electrodes were placed on the brain surface, including the ROIs, using a navigation system. Cortico-cortical evoked potentials (CCEPs) were evoked by applying electrical stimuli to the hand region of M1 using electrodes placed on the convexity and were measured with electrodes placed on the interhemispheric fissure. To verify CCEP bidirectionality, electrical stimuli were applied to electrodes on the interhemispheric fissure that showed CCEP responses. Correlations of CCEP amplitudes and latencies with the number, mean length, and mean FA value obtained from tractography were determined. The correlations between these parameters and perioperative motor functions were also analyzed. Fibers of 14 patients were visualized by diffusion tensor imaging (DTI). Unidirectional CCEPs between the PCL and M1 were measurable in all 16 patients, and bidirectional CCEPs between them were measurable in 14 patients. There was no significant difference between the two directions in the maximum CCEP amplitude or latency (amplitude, p = 0.391; latency, p = 0.583). Neither the amplitude nor latency showed any apparent correlation with the number, mean length, or mean FA value of the fibers obtained from tractography. Pre- and postoperative motor function of the hands was not significantly correlated with CCEP amplitude or latency. The number and mean FA value of fibers obtained by DTI, as well as the maximum CCEP amplitude, varied between patients. This study demonstrated an anatomical connection and a bidirectional functional connection between the PCL, including the supplementary motor area, and M1 of the human brain. The observed variability between patients suggests possible motor function plasticity. These findings may serve as a foundation for further studies.

The effects of slow wave sleep characteristics on semantic, episodic, and procedural memory in people with epilepsy.

Slow wave sleep (SWS) is highly relevant for verbal and non-verbal/spatial memory in healthy individuals, but also in people with epilepsy. However, contradictory findings exist regarding the effect of seizures on overnight memory retention, particularly relating to procedural and non-verbal memory, and thorough examination of episodic memory retention with ecologically valid tests is missing. This research explores the interaction of SWS duration with epilepsy-relevant factors, as well as the relation of spectral characteristics of SWS on overnight retention of procedural, verbal, and episodic memory. In an epilepsy monitoring unit, epilepsy patients (N = 40) underwent learning, immediate and 12h delayed testing of memory retention for a fingertapping task (procedural memory), a word-pair task (verbal memory), and an innovative virtual reality task (episodic memory). We used multiple linear regression to examine the impact of SWS duration, spectral characteristics of SWS, seizure occurrence, medication, depression, seizure type, gender, and epilepsy duration on overnight memory retention. Results indicated that none of the candidate variables significantly predicted overnight changes for procedural memory performance. For verbal memory, the occurrence of tonic-clonic seizures negatively impacted memory retention and higher psychoactive medication load showed a tendency for lower verbal memory retention. Episodic memory was significantly impacted by epilepsy duration, displaying a potential nonlinear impact with a longer duration than 10years negatively affecting memory performance. Higher drug load of anti-seizure medication was by tendency related to better overnight retention of episodic memory. Contrary to expectations longer SWS duration showed a trend towards decreased episodic memory performance. Analyses on associations between memory types and EEG band power during SWS revealed lower alpha-band power in the frontal right region as significant predictor for better episodic memory retention. In conclusion, this research reveals that memory modalities are not equally affected by important epilepsy factors such as duration of epilepsy and medication, as well as SWS spectral characteristics.

ATST-Net: A method to identify early symptoms in the upper and lower extremities of PD

Bradykinesia, a core symptom of motor disorders in Parkinson's disease (PD), is a major criterion for screening early PD patients in clinical practice. Currently, many studies have proposed automatic assessment schemes for bradykinesia in PD. However, existing schemes suffer from problems such as dependence on professional equipment, single evaluation tasks, difficulty in obtaining samples and low accuracy. This paper proposes a manual feature extraction- and neural network-based method to evaluate bradykinesia, effectively solving the problem of a small sample size. This method can automatically assess finger tapping (FT), hand movement (HM), toe tapping (TT) and bilateral foot sensitivity tasks (LA) through a unified model. Data were obtained from 120 individuals, including 93 patients with Parkinson's disease and 27 age- and sex-matched normal controls (NCs). Manual feature extraction and Attention Time Series Two-stream Networks (ATST-Net) were used for classification. Accuracy rates of 0.844, 0.819, 0.728, and 0.768 were achieved for FT, HM, TT, and LA, respectively. To our knowledge, this study is the first to simultaneously evaluate the upper and lower limbs using a unified model that has significant advantages in both model training and transfer learning.

Audiovisual integration of rhythm in musicians and dancers.

Music training is associated with better beat processing in the auditory modality. However, it is unknown how rhythmic training that emphasizes visual rhythms, such as dance training, might affect beat processing, nor whether training effects in general are modality specific. Here we examined how music and dance training interacted with modality during audiovisual integration and synchronization to auditory and visual isochronous sequences. In two experiments, musicians, dancers, and controls completed an audiovisual integration task and an audiovisual target-distractor synchronization task using dynamic visual stimuli (a bouncing figure). The groups performed similarly on the audiovisual integration tasks (Experiments 1 and 2). However, in the finger-tapping synchronization task (Experiment 1), musicians were more influenced by auditory distractors when synchronizing to visual sequences, while dancers were more influenced by visual distractors when synchronizing to auditory sequences. When participants synchronized with whole-body movements instead of finger-tapping (Experiment 2), all groups were more influenced by the visual distractor than the auditory distractor. Taken together, this study highlights how training is associated with audiovisual processing, and how different types of visual rhythmic stimuli and different movements alter beat perception and production outcome measures. Implications for the modality appropriateness hypothesis are discussed.

Negative emotions disrupt intentional synchronization during group sensorimotor interaction.

Emotions play a fundamental role in human interactions and trigger responses in physiological, psychological, and behavioral modalities. Interpersonal coordination often entails attunement between individuals in various modalities. Previous research has elucidated the mechanisms of interpersonal synchronization and the emotions aroused by joint action: cardiac activity aligns in disputing marital couples, spectators share enjoyment in observing live dance performances, and joint finger-tapping evokes positive emotions. However, little is known about the impact of emotions on intentional interpersonal synchronization. To address this problem, we conducted an experiment in 2022 asking 60 participants to engage in a three-way finger-tapping synchronization task. We systematically induced emotional states (positive, neutral, and negative) with social comparison feedback using success-failure manipulations. An analysis of behavior synchronization using the Kuramoto order parameter revealed that negative emotion induction significantly diminished time spent in synchrony compared to positive induction. Moreover, the results exposed incremental struggles in attaining higher levels of synchronization (Q2-Q3) after the induction of negative emotions. These outcomes further substantiate the necessity of integrating the indices of agents' emotions into interpersonal synchronization and coordination models. We discuss the implications of this work for research on interpersonal emotion in joint action and applied outcomes in emotion-aware technologies and interventions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparing speech to fine and gross motor skills in Parkinson’s patients

Parkinson’s Disease (PD) is a neurodegenerative motor disorder resulting from damage to dopaminergic neurons. The goal of this study is to evaluate the correspondence between speech and non-speech motor impairments. To explore this, we extract features from the mPower dataset [B. M. Bot et al., Sci Data 3, 160011 (2016)] containing mobile data from PD patients and healthy controls along with their performance on a vowel phonation, finger tapping, and walking task. We hypothesize that there is a shared motor system underlying each of these modalities and that disease progression will manifest in impairments to both speech and non-speech systems that rely on motor control. For acoustic features, we measure temporal consistency via F0-independent features (shimmer, jitter, and harmonics-to-noise ratio). For non-acoustic tasks, we adapt this set to measure spatial consistency and accuracy in finger tapping or walking. We perform clustering and multidimensional scaling (MDS) on our features to understand their correspondence across the modalities. Results will be reported with relevance to the relationship between PD and its effects on articulatory and general motor processes.

Unleashing the potential of fNIRS with machine learning: classification of fine anatomical movements to empower future brain-computer interface.

In this study, we explore the potential of using functional near-infrared spectroscopy (fNIRS) signals in conjunction with modern machine-learning techniques to classify specific anatomical movements to increase the number of control commands for a possible fNIRS-based brain-computer interface (BCI) applications. The study focuses on novel individual finger-tapping, a well-known task in fNIRS and fMRI studies, but limited to left/right or few fingers. Twenty-four right-handed participants performed the individual finger-tapping task. Data were recorded by using sixteen sources and detectors placed over the motor cortex according to the 10-10 international system. The event's average oxygenated Δ HbO and deoxygenated Δ HbR hemoglobin data were utilized as features to assess the performance of diverse machine learning (ML) models in a challenging multi-class classification setting. These methods include LDA, QDA, MNLR, XGBoost, and RF. A new DL-based model named "Hemo-Net" has been proposed which consists of multiple parallel convolution layers with different filters to extract the features. This paper aims to explore the efficacy of using fNRIS along with ML/DL methods in a multi-class classification task. Complex models like RF, XGBoost, and Hemo-Net produce relatively higher test set accuracy when compared to LDA, MNLR, and QDA. Hemo-Net has depicted a superior performance achieving the highest test set accuracy of 76%, however, in this work, we do not aim at improving the accuracies of models rather we are interested in exploring if fNIRS has the neural signatures to help modern ML/DL methods in multi-class classification which can lead to applications like brain-computer interfaces. Multi-class classification of fine anatomical movements, such as individual finger movements, is difficult to classify with fNIRS data. Traditional ML models like MNLR and LDA show inferior performance compared to the ensemble-based methods of RF and XGBoost. DL-based method Hemo-Net outperforms all methods evaluated in this study and demonstrates a promising future for fNIRS-based BCI applications.

Rhythmic motor behavior explains individual differences in grammar skills in adults

A growing body of literature has reported the relationship between music and language, particularly between individual differences in perceptual rhythm skill and grammar competency in children. Here, we investigated whether motoric aspects of rhythm processing—as measured by rhythmic finger tapping tasks—also explain the rhythm-grammar connection in 150 healthy young adults. We found that all expressive rhythm skills (spontaneous, synchronized, and continued tapping) along with rhythm discrimination skill significantly predicted receptive grammar skills on either auditory sentence comprehension or grammaticality well-formedness judgment (e.g., singular/plural, past/present), even after controlling for verbal working memory and music experience. Among these, synchronized tapping and rhythm discrimination explained unique variance of sentence comprehension and grammaticality judgment, respectively, indicating differential associations between different rhythm and grammar skills. Together, we demonstrate that even simple and repetitive motor behavior can account for seemingly high-order grammar skills in the adult population, suggesting that the sensorimotor system continue to support syntactic operations.

Maintenance of Procedural Motor Memory across Brief Rest Periods Requires the Hippocampus.

Research on the role of the hippocampus in memory acquisition has generally focused on active learning. But to understand memory, it is at least as important to understand processes that happen offline, during both wake and sleep. In a study of patients with amnesia, we previously demonstrated that although a functional hippocampus is not necessary for the acquisition of procedural motor memory during training session, it is required for its offline consolidation during sleep. Here, we investigated whether an intact hippocampus is also required for the offline consolidation of procedural motor memory while awake. Patients with amnesia due to hippocampal damage (n = 4, all male) and demographically matched controls (n = 10, 8 males) trained on the finger tapping motor sequence task. Learning was measured as gains in typing speed and was divided into online (during task execution) and offline (during interleaved 30 s breaks) components. Amnesic patients and controls showed comparable total learning, but differed in the pattern of performance improvement. Unlike younger adults, who gain speed across breaks, both groups gained speed only while typing. Only controls retained these gains over the breaks; amnesic patients slowed down and compensated for these losses during subsequent typing. In summary, unlike their peers, whose motor performance remained stable across brief breaks in typing, amnesic patients showed evidence of impaired access to motor procedural memory. We conclude that in addition to being necessary for the offline consolidation of motor memories during sleep, the hippocampus maintains access to motor memory across brief offline periods during wake.

CTBS over primary motor cortex increased contralateral corticomuscular coupling and interhemispheric functional connection

Objective. Transcranial magnetic stimulation is a non-invasive brain stimulation technique that changes the activity of the cerebral cortex. Contralesional continuous theta burst stimulation (cTBS) has been proposed and verified beneficial to stroke motor recovery. However, the underlying mechanism is still unclear. Approach. 20 healthy right-handed subjects were recruited in this study, receiving real-cTBS over their left primary motor cortex or sham-cTBS. We designed the finger tapping task (FTT) before and after stimulation and recorded the accuracy and reaction time (RT) of the task. The electroencephalogram and surface electromyogram signals were recorded during the left finger pinching task (FPT) before and after stimulation. We calculated cortico-muscular coherence (CMC) in the contralateral hemisphere and cortico-cortical coherence (CCC) in the bilateral hemisphere. The two-way repeated measures analysis of variance was used to analyze the effect of cTBS. Main results. In the FTT, there was a significant main effect of ‘time’ on RT (F(1, 38) = 24.739, p < 0.001). In the FPT, the results showed that there was a significant interaction effect on the CMC peak and area in the beta band (peak: F(1, 38) = 8.562, p = 0.006; area: F(1, 38) = 5.273, p = 0.027), on the CCC peak in the alpha band (F(1, 38) = 4.815, p = 0.034) and area in the beta band (F(1, 38) = 4.822, p= 0.034). The post hoc tests showed that the CMC peak (W = 20, p = 0.002), the CMC area (W = 13, p = 0.003) and the CCC peak (t = −2.696, p = 0.014) increased significantly after real-cTBS. However, there was no significant decrease or increase after sham-cTBS. Significance. Our study found that cTBS can improve CMC of contralateral hemisphere and CCC of bilateral hemisphere, indicating that cTBS can strengthen cortico-muscular and cortico-cortical coupling.

Time-varying functional connectivity predicts fluctuations in sustained attention in a serial tapping task.

The mechanisms for how large-scale brain networks contribute to sustained attention are unknown. Attention fluctuates from moment to moment, and this continuous change is consistent with dynamic changes in functional connectivity between brain networks involved in the internal and external allocation of attention. In this study, we investigated how brain network activity varied across different levels of attentional focus (i.e., "zones"). Participants performed a finger-tapping task, and guided by previous research, in-the-zone performance or state was identified by low reaction time variability and out-of-the-zone as the inverse. In-the-zone sessions tended to occur earlier in the session than out-of-the-zone blocks. This is unsurprising given the way attention fluctuates over time. Employing a novel method of time-varying functional connectivity, called the quasi-periodic pattern analysis (i.e., reliable, network-level low-frequency fluctuations), we found that the activity between the default mode network (DMN) and task positive network (TPN) is significantly more anti-correlated during in-the-zone states versus out-of-the-zone states. Furthermore, it is the frontoparietal control network (FPCN) switch that differentiates the two zone states. Activity in the dorsal attention network (DAN) and DMN were desynchronized across both zone states. During out-of-the-zone periods, FPCN synchronized with DMN, while during in-the-zone periods, FPCN switched to synchronized with DAN. In contrast, the ventral attention network (VAN) synchronized more closely with DMN during in-the-zone periods compared with out-of-the-zone periods. These findings demonstrate that time-varying functional connectivity of low frequency fluctuations across different brain networks varies with fluctuations in sustained attention or other processes that change over time.

Altered functional connectivity in the hippocampal and striatal systems after motor sequence learning consolidation in medial temporal lobe epilepsy individuals.

Both the hippocampal and striatal systems participate in motor sequence learning (MSL) in healthy subjects, and the prominent role of the hippocampal system in sleep-related consolidation has been demonstrated. However, some pathological states may change the functional dominance between these two systems in MSL consolidation. To better understand the functional performance within these two systems under the pathological condition of hippocampal impairment, we compared the functional differences after consolidation between patients with left medial temporal lobe epilepsy (LmTLE) and healthy control subjects (HCs). We assessed participants' performance on the finger-tapping task (FTT) during acquisition (on day 1) and after consolidation during sleep (on day 2). All participants underwent an MRI scan (T1 and resting state) before each FTT. We found that the LmTLE group showed performance deficits in offline consolidation compared to the HC group. The LmTLE group exhibited structural changes, such as decreased gray matter volume (GMV) in the left hippocampus and increased GMV in the right putamen (striatum). Our results also revealed that whereas the main effect of consolidation was observed in the hippocampus-related functional connection in the HC group, it was only evident in the striatum-related functional loop in the LmTLE group. Our findings indicated that LmTLE patients may rely more on the striatal system for offline consolidation because of structural impairments in the hippocampus. Additionally, this compensatory mechanism may not fully substitute for the role of the impaired hippocampus itself.NEW & NOTEWORTHY Motor sequence learning (MSL) relies on both the hippocampal and striatal systems, but whether functional performance is altered after MSL consolidation when the hippocampus is impaired remains unknown. Our results indicated that whereas the main effect of consolidation was observed in the hippocampus-related functional connection in the healthy control (HC) group, it was only evident in the striatum-related functional loop in the left medial temporal lobe epilepsy (LmTLE) group.

Effects of Rhythm and Accent Patterns on Tempo-Keeping Property of Finger Tapping

Tempo of music performance is often accelerated irrespective of players’ intention. Though the characteristics of the tempo deviation phenomenon have been investigated using a finger-tapping task, most such studies dealt with tapping with a fixed interval and intensity; few studies considered the effects of rhythm and accent, both important factors of music performance. Here, we asked how different rhythm and accent patterns affected the tempo-keeping property using a synchronization-continuation task paradigm: Participants were asked to keep tapping with reproducing the given rhythm/accent patterns designated by the target tones. Tapping tempo was significantly deviated depending on rhythm/accent patterns, but their magnitudes were only several percent in 150 s, much smaller than those in real music performance. We also ran experiments in the conditions that participants need not reproduce the accent patterns but only response tones were accented. These auditory modulations affected the tempo deviation, implying that perceptual processes induced by the auditory feedback also influence the tempo maintenance. Inter-tap intervals (ITIs) were modulated by the accent pattern, but no clear relationship was observed between ITI fluctuations and long-term tempo deviation, suggesting that different mechanisms are involved. In sum, the present finding shows that non-uniform motor actions required for music articulation can disturb the long-term tempo maintenance but their effect is rather small. We also reported the tempo deviation when the response tone was modulated by loudness (crescendo vs. diminuendo) or pitch (ascending vs. descending).

Increased dual-task interference during upper limb movements in stroke exceeding that found in aging - a systematic review and meta-analysis.

To determine whether dual-task interference during upper limb tasks is increased in patients after stroke compared to healthy older subjects and to compare magnitude of stroke-induced change in interference to that explained by aging. We conducted a systematic literature search in MEDLINE, CINAHL, Google Scholar and PEDro databases up to October 2023 for studies on upper limb dual-tasks in stroke and elderly healthy subjects. Eleven upper limb dual-task studies in stroke patients and 11 studies in healthy older subjects were identified and systematically reviewed. A meta-analysis was performed on seven stroke studies and on five studies in healthy older subjects that included control groups. Most stroke studies investigated proximal arm movements with kinematic measures, but few studies evaluated manual dexterity. In contrast, studies in healthy older subjects used more distal (finger tapping) tasks. The meta-analysis showed that stroke patients had on average a 19% (CI 95% = 1.0-37.3) increase in dual-task interference compared to age-matched healthy controls (Z = 2.06, p = 0.04). Older healthy subjects showed greater dual-task interference compared to younger subjects (19% greater, CI 95% = 6.5-31.2, Z = 2.98, p = 0.003). Meta-analysis revealed an increase in dual-task interference during upper limb movements in stroke patients, exceeding age-related changes, supporting the presence of subclinical impairments in divided attention post-stroke that may impede motor recovery.

Estimation of motor severity scales in Parkinson’s disease by linear models of bimanual non-alternating index finger tapping features

Background and ObjectiveClinical scales used by well-trained clinicians to assess motor symptoms in patients with Parkinson’s Disease (PD) allow to establish the patients’ medical therapy and follow-up their response. However, these assessments are subjective and their application to patients requires experienced and qualified operators. This study analyzes the role of the kinematic patient’s features, captured by a simple computer keyboard paradigm, in predicting the scores prescribed by an experienced neurologist. MethodsA total of 47 patients in their ON medication state participated in this study. Their motor capacity was assessed by an experienced neurologist with several standardized clinical scales. The patients also performed 5 consecutive trials of 10 s of a computerized finger tapping task by pressing with their index the space bar, first with their dominant hand and then with the other hand. 270 tapping-related features were extracted from the tapping task data for each participant and linear regression multivariate models for each clinical variable were built by using these features. ResultsThe best resulting models were for the motor capacity (Unified Parkisnon Disase Scale Revised – MDS-UPDRS Part III), years from disease onset and balance scores (Limit of Stability – LoS), with root mean squared errors (RMSE) of 0.268, 0.254 and 0.150, respectively, all bellow their corresponding minimal clinically important differences. Those models included variables from both hands and from all trials, mainly regarding slow and fast tapping-related variables in different degrees. ConclusionsA simple bimanual non-alternating finger tapping task has shown to foresee motor capacity and balance scores by using statistical and machine learning methods. This easy and quick task could be performed periodically in the medical office or at home helping the clinician to know the patients’ motor state and temporary alterations in that way and to make finer clinical decisions about the proper pharmacological treatment of every patient.

Task-related brain activity in upper limb dystonia revealed by simultaneous fNIRS and EEG

ObjectiveThe aim of this study was to explore differences in brain activity and connectivity using simultaneous electroencephalography and near-infrared spectroscopy in patients with focal dystonia during handwriting and finger-tapping tasks. MethodsPatients with idiopathic right upper limb focal dystonia and controls were assessed by simultaneous near-infrared spectroscopy and electroencephalography during the writing and finger-tapping tasks in terms of the mu-alpha, mu-beta, beta and low gamma power and effective connectivity, as well as relative changes in oxyhemoglobin (oxy-Hb) and deoxyhemoglobin using a channel-wise approach with a mixed-effect model. ResultsPatients exhibited higher oxy-Hb levels in the right and left motor cortex and supplementary motor area during writing, but lower oxy-Hb levels in the left sensorimotor and bilateral somatosensory area during finger-tapping compared to controls. During writing, patients showed increased low gamma power in the bilateral sensorimotor cortex and less mu-beta and beta attenuation compared to controls. Additionally, patients had reduced connectivity between the supplementary motor area and the left sensorimotor cortex during writing. No differences were observed in terms of effective connectivity in either task. Finally, patients failed to attenuate the mu-alpha, mu-beta, and beta rhythms during the finger-tapping task. ConclusionsCortical blood flow and EEG spectral power differ between controls and dystonia patients, depending on the task. Writing increased blood flow and altered connectivity in dystonia patients, and it also decreased slow-band attenuation. Finger-tapping decreased blood flow and slow-band attenuation. SignificanceSimultaneous fNIRS and EEG may show relevant information regarding brain dynamics in movement disorders patients in unconstrained environments.

An acute bout of high-intensity exercise affects nocturnal sleep and sleep-dependent memory consolidation.

Acute exercise has been shown to affect long-term memory and sleep. However, it is unclear whether exercise-induced changes in sleep architecture are associated with enhanced memory. Recently, it has been shown that exercise followed by a nap improved declarative memory. Whether these effects transfer to night sleep and other memory domains has not yet been studied. Here, we investigate the influence of exercise on nocturnal sleep architecture and associations with sleep-dependent procedural and declarative memory consolidation. Nineteen subjects (23.68 ± 3.97 years) were tested in a balanced cross-over design. In two evening sessions, participants either exercised (high-intensity interval training) or rested immediately after encoding two memory tasks: (1) a finger tapping task and (2) a paired-associate learning task. Subsequent nocturnal sleep was recorded by polysomnography. Retrieval was conducted the following morning. High-intensity interval training lead to an increased declarative memory retention (p = 0.047, d = 0.40) along with a decrease in REM sleep (p = 0.012, d = 0.75). Neither procedural memory nor NREM sleep were significantly affected. Exercise-induced changes in N2 showed a positive correlation with procedural memory retention which did not withstand multiple comparison correction. Exploratory analyses on sleep spindles and slow wave activity did not reveal significant effects. The present findings suggest an exercise-induced enhancement of declarative memory which aligns with changes in nocturnal sleep architecture. This gives additional support for the idea of a potential link between exercise-induced sleep modifications and memory formation which requires further investigation in larger scaled studies.

The ability to maintain rhythm is predictive of ADHD diagnosis and profile

Attention deficit hyperactivity disorder (ADHD) is the most prevalent neuropsychiatric disorder in the world. Currently, the diagnosis is based mainly on interviews, resulting in uncertainties in the clinical assessment. While some neuropsychological tests are used, their specificity and selectivity are low, and more reliable biomarkers are desirable. Previous research indicates that ADHD is associated with morphological changes in the cerebellum, which is essential for motor ability and timing. Here, we compared 29 children diagnosed with ADHD to 96 age-matched controls on prism adaptation, eyeblink conditioning, and timed motor performance in a finger tapping task. Prism adaptation and timing precision in the finger tapping task, but not performance on eyeblink conditioning, differed between the ADHD and control groups, as well as between children with and without Deficits in Attention, Motor control, and Perception (DAMP) – a more severe form of ADHD. The results suggest finger tapping can be used as a cheap, objective, and unbiased biomarker to complement current diagnostic procedures.

Transcranial magnetic stimulation mapping of the motor cortex: comparison of five estimation algorithms.

There are currently five different kinds of transcranial magnetic stimulation (TMS) motor mapping algorithms available, from ordinary point-based algorithms to advanced field-based algorithms. However, there have been only a limited number of comparison studies conducted, and they have not yet examined all of the currently available algorithms. This deficiency impedes the judicious selection of algorithms for application in both clinical and basic neuroscience, and hinders the potential promotion of a potential superior algorithm. Considering the influence of algorithm complexity, further investigation is needed to examine the differences between fMRI peaks and TMS cortical hotspots that were identified previously. Twelve healthy participants underwent TMS motor mapping and a finger-tapping task during fMRI. The motor cortex TMS mapping results were estimated by five algorithms, and fMRI activation results were obtained. For each algorithm, the prediction error was defined as the distance between the measured scalp hotspot and optimized coil position, which was determined by the maximum electric field strength in the estimated motor cortex. Additionally, the study identified the minimum number of stimuli required for stable mapping. Finally, the location difference between the TMS mapping cortical hotspot and the fMRI activation peak was analyzed. The projection yielded the lowest prediction error (5.27 ± 4.24 mm) among the point-based algorithms and the association algorithm yielded the lowest (6.66 ± 3.48 mm) among field-based estimation algorithms. The projection algorithm required fewer stimuli, possibly resulting from its suitability for the grid-based mapping data collection method. The TMS cortical hotspots from all algorithms consistently deviated from the fMRI activation peak (20.52 ± 8.46 mm for five algorithms). The association algorithm might be a superior choice for clinical applications and basic neuroscience research, due to its lower prediction error and higher estimation sensitivity in the deep cortical structure, especially for the sulcus. It also has potential applicability in various other TMS domains, including language area mapping and more. Otherwise, our results provide further evidence that TMS motor mapping intrinsically differs from fMRI motor mapping.