The recent history of afferent stimulation modulates corticospinal excitability.

BACKGROUND: Transcranial magnetic stimulation (TMS) is widely used to probe corticospinal excitability and fast sensorimotor integration in the primary motor hand area (M1-HAND). A conditioning electrical stimulus, applied to the contralateral hand, can suppress the motor evoked potential (MEP) elicited by TMS of M1-HAND when the afferent stimulus arrives in M1-HAND at the time of TMS. The magnitude of this short-latency afferent inhibition (SAI) is expressed as the ratio between the conditioned and unconditioned MEP amplitude. OBJECTIVE/HYPOTHESIS: We hypothesized that corticospinal excitability and SAI are influenced by the recent history of peripheral electrical stimulation. METHODS: In twenty healthy participants, we recorded MEPs from the right first dorsal interosseus muscle. MEPs were evoked by single-pulse TMS of the left M1-HAND alone (unconditioned TMS) or by TMS preceded by electrical stimulation of the right index finger ("homotopic" conditioning) or little finger ("heterotopic" conditioning). The three conditions were either pseudo-randomly intermixed or delivered in blocks in which a single condition was repeated five or ten times. MEP amplitudes and SAI magnitudes were compared using linear mixed-effect models and one-way ANOVAs. RESULTS: All stimulation protocols consistently produced SAI, which was stronger after homotopic stimulation. Randomly intermingling the three stimulation conditions reduced the relative magnitude of homotopic and heterotopic SAI as opposed to blocked stimulation. The apparent attenuation of SAI was caused by a suppression of the unconditioned but not the conditioned MEP amplitude during the randomly intermixed pattern. CONCLUSION(S): The recent history of afferent stimulation modulates corticospinal excitability. This "history effect" impacts on the relative magnitude of SAI depending on how conditioned and unconditioned responses are intermixed and needs to be taken into consideration when probing afferent inhibition and corticospinal excitability.

Mapping cortico-subcortical sensitivity to 4 Hz amplitude modulation depth in human auditory system with functional MRI.

Temporal modulations in the envelope of acoustic waveforms at rates around 4 Hz constitute a strong acoustic cue in speech and other natural sounds. It is often assumed that the ascending auditory pathway is increasingly sensitive to slow amplitude modulation (AM), but sensitivity to AM is typically considered separately for individual stages of the auditory system. Here, we used blood oxygen level dependent (BOLD) fMRI in twenty human subjects (10 male) to measure sensitivity of regional neural activity in the auditory system to 4 Hz temporal modulations. Participants were exposed to AM noise stimuli varying parametrically in modulation depth to characterize modulation-depth effects on BOLD responses. A Bayesian hierarchical modeling approach was used to model potentially nonlinear relations between AM depth and group-level BOLD responses in auditory regions of interest (ROIs). Sound stimulation activated the auditory brainstem and cortex structures in single subjects. BOLD responses to noise exposure in core and belt auditory cortices scaled positively with modulation depth. This finding was corroborated by whole-brain cluster-level inference. Sensitivity to AM depth variations was particularly pronounced in the Heschl's gyrus but also found in higher-order auditory cortical regions. None of the sound-responsive subcortical auditory structures showed a BOLD response profile that reflected the parametric variation in AM depth. The results are compatible with the notion that early auditory cortical regions play a key role in processing low-rate modulation content of sounds in the human auditory system.

Locus Coeruleus Shows a Spatial Pattern of Structural Disintegration in Parkinson's Disease.

BACKGROUND: Parkinson's disease (PD) causes a loss of neuromelanin-positive, noradrenergic neurons in the locus coeruleus (LC), which has been implicated in nonmotor dysfunction. OBJECTIVES: We used "neuromelanin sensitive" magnetic resonance imaging (MRI) to localize structural disintegration in the LC and its association with nonmotor dysfunction in PD. METHODS: A total of 42 patients with PD and 24 age-matched healthy volunteers underwent magnetization transfer weighted (MTw) MRI of the LC. The contrast-to-noise ratio of the MTw signal (CNRMTw ) was used as an index of structural LC integrity. We performed slicewise and voxelwise analyses to map spatial patterns of structural disintegration, complemented by principal component analysis (PCA). We also tested for correlations between regional CNRMTw and severity of nonmotor symptoms. RESULTS: Mean CNRMTw of the right LC was reduced in patients relative to controls. Voxelwise and slicewise analyses showed that the attenuation of CNRMTw was confined to the right mid-caudal LC and linked regional CNRMTw to nonmotor symptoms. CNRMTw attenuation in the left mid-caudal LC was associated with the orthostatic drop in systolic blood pressure, whereas CNRMTw attenuation in the caudal most portion of right LC correlated with apathy ratings. PCA identified a bilateral component that was more weakly expressed in patients. This component was characterized by a gradient in CNRMTw along the rostro-caudal and dorso-ventral axes of the nucleus. The individual expression score of this component reflected the overall severity of nonmotor symptoms. CONCLUSION: A spatially heterogeneous disintegration of LC in PD may determine the individual expression of specific nonmotor symptoms such as orthostatic dysregulation or apathy. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society.

Effects of Sensorineural Hearing Loss on Cortical Synchronization to Competing Speech during Selective Attention.

When selectively attending to a speech stream in multi-talker scenarios, low-frequency cortical activity is known to synchronize selectively to fluctuations in the attended speech signal. Older listeners with age-related sensorineural hearing loss (presbycusis) often struggle to understand speech in such situations, even when wearing a hearing aid. Yet, it is unclear whether a peripheral hearing loss degrades the attentional modulation of cortical speech tracking. Here, we used psychoacoustics and electroencephalography (EEG) in male and female human listeners to examine potential effects of hearing loss on EEG correlates of speech envelope synchronization in cortex. Behaviorally, older hearing-impaired (HI) listeners showed degraded speech-in-noise recognition and reduced temporal acuity compared with age-matched normal-hearing (NH) controls. During EEG recordings, we used a selective attention task with two spatially separated simultaneous speech streams where NH and HI listeners both showed high speech recognition performance. Low-frequency (<10 Hz) envelope-entrained EEG responses were enhanced in the HI listeners, both for the attended speech, but also for tone sequences modulated at slow rates (4 Hz) during passive listening. Compared with the attended speech, responses to the ignored stream were found to be reduced in both HI and NH listeners, allowing for the attended target to be classified from single-trial EEG data with similar high accuracy in the two groups. However, despite robust attention-modulated speech entrainment, the HI listeners rated the competing speech task to be more difficult. These results suggest that speech-in-noise problems experienced by older HI listeners are not necessarily associated with degraded attentional selection.SIGNIFICANCE STATEMENT People with age-related sensorineural hearing loss often struggle to follow speech in the presence of competing talkers. It is currently unclear whether hearing impairment may impair the ability to use selective attention to suppress distracting speech in situations when the distractor is well segregated from the target. Here, we report amplified envelope-entrained cortical EEG responses to attended speech and to simple tones modulated at speech rates (4 Hz) in listeners with age-related hearing loss. Critically, despite increased self-reported listening difficulties, cortical synchronization to speech mixtures was robustly modulated by selective attention in listeners with hearing loss. This allowed the attended talker to be classified from single-trial EEG responses with high accuracy in both older hearing-impaired listeners and age-matched normal-hearing controls.

Cortical oscillations and entrainment in speech processing during working memory load.

Neuronal oscillations are thought to play an important role in working memory (WM) and speech processing. Listening to speech in real-life situations is often cognitively demanding but it is unknown whether WM load influences how auditory cortical activity synchronizes to speech features. Here, we developed an auditory n-back paradigm to investigate cortical entrainment to speech envelope fluctuations under different degrees of WM load. We measured the electroencephalogram, pupil dilations and behavioural performance from 22 subjects listening to continuous speech with an embedded n-back task. The speech stimuli consisted of long spoken number sequences created to match natural speech in terms of sentence intonation, syllabic rate and phonetic content. To burden different WM functions during speech processing, listeners performed an n-back task on the speech sequences in different levels of background noise. Increasing WM load at higher n-back levels was associated with a decrease in posterior alpha power as well as increased pupil dilations. Frontal theta power increased at the start of the trial and increased additionally with higher n-back level. The observed alpha-theta power changes are consistent with visual n-back paradigms suggesting general oscillatory correlates of WM processing load. Speech entrainment was measured as a linear mapping between the envelope of the speech signal and low-frequency cortical activity (< 13 Hz). We found that increases in both types of WM load (background noise and n-back level) decreased cortical speech envelope entrainment. Although entrainment persisted under high load, our results suggest a top-down influence of WM processing on cortical speech entrainment.

Multiway canonical correlation analysis of brain data.

Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.

Noise-robust cortical tracking of attended speech in real-world acoustic scenes.

Selectively attending to one speaker in a multi-speaker scenario is thought to synchronize low-frequency cortical activity to the attended speech signal. In recent studies, reconstruction of speech from single-trial electroencephalogram (EEG) data has been used to decode which talker a listener is attending to in a two-talker situation. It is currently unclear how this generalizes to more complex sound environments. Behaviorally, speech perception is robust to the acoustic distortions that listeners typically encounter in everyday life, but it is unknown whether this is mirrored by a noise-robust neural tracking of attended speech. Here we used advanced acoustic simulations to recreate real-world acoustic scenes in the laboratory. In virtual acoustic realities with varying amounts of reverberation and number of interfering talkers, listeners selectively attended to the speech stream of a particular talker. Across the different listening environments, we found that the attended talker could be accurately decoded from single-trial EEG data irrespective of the different distortions in the acoustic input. For highly reverberant environments, speech envelopes reconstructed from neural responses to the distorted stimuli resembled the original clean signal more than the distorted input. With reverberant speech, we observed a late cortical response to the attended speech stream that encoded temporal modulations in the speech signal without its reverberant distortion. Single-trial attention decoding accuracies based on 40-50s long blocks of data from 64 scalp electrodes were equally high (80-90% correct) in all considered listening environments and remained statistically significant using down to 10 scalp electrodes and short (<30-s) unaveraged EEG segments. In contrast to the robust decoding of the attended talker we found that decoding of the unattended talker deteriorated with the acoustic distortions. These results suggest that cortical activity tracks an attended speech signal in a way that is invariant to acoustic distortions encountered in real-life sound environments. Noise-robust attention decoding additionally suggests a potential utility of stimulus reconstruction techniques in attention-controlled brain-computer interfaces.

Patient-tailored transcranial direct current stimulation to improve stroke rehabilitation: study protocol of a randomized sham-controlled trial.

BACKGROUND: Many patients do not fully regain motor function after ischemic stroke. Transcranial direct current stimulation (TDCS) targeting the motor cortex may improve motor outcome as an add-on intervention to physical rehabilitation. However, beneficial effects on motor function vary largely among patients within and across TDCS trials. In addition to a large heterogeneity of study designs, this variability may be caused by the fact that TDCS was given as a one-size-fits-all protocol without accounting for anatomical differences between subjects. The efficacy and consistency of TDCS might be improved by a patient-tailored design that ensures precise targeting of a physiologically relevant area with an appropriate current strength. METHODS: In a randomized, double-blinded, sham-controlled trial, patients with subacute ischemic stroke and residual upper-extremity paresis will receive two times 20 min of focal TDCS of ipsilesional primary motor hand area (M1-HAND) during supervised rehabilitation training three times weekly for 4 weeks. Anticipated 60 patients will be randomly assigned to active or sham TDCS of ipsilesional M1-HAND, using a central anode and four equidistant cathodes. The placement of the electrode grid on the scalp and current strength at each cathode will be personalized based on individual electrical field models to induce an electrical current of 0.2 V/m in the cortical target region resulting in current strengths between 1 and 4 mA. Primary endpoint will be the difference in change of Fugl-Meyer Assessment of Upper Extremity (FMA-UE) score between active TDCS and sham at the end of the intervention. Exploratory endpoints will include UE-FMA at 12 weeks. Effects of TDCS on motor network connectivity and interhemispheric inhibition will be assessed with functional MRI and transcranial magnetic stimulation. DISCUSSION: The study will show the feasibility and test the efficacy of personalized, multi-electrode anodal TDCS of M1-HAND in patients with subacute stroke patients with upper-extremity paresis. Concurrent multimodal brain mapping will shed light into the mechanisms of action of therapeutic personalized TDCS of M1-HAND. Together, the results from this trial may inform future personalized TDCS studies in patients with focal neurological deficits after stroke.

Executive Control and Associated Brain Activity in Children With Familial High-Risk of Schizophrenia or Bipolar Disorder: A Danish Register-based Study.

BACKGROUND AND HYPOTHESES: Impaired executive control is a potential prognostic and endophenotypic marker of schizophrenia (SZ) and bipolar disorder (BP). Assessing children with familial high-risk (FHR) of SZ or BP enables characterization of early risk markers and we hypothesize that they express impaired executive control as well as aberrant brain activation compared to population-based control (PBC) children. STUDY DESIGN: Using a flanker task, we examined executive control together with functional magnetic resonance imaging (fMRI) in 11- to 12-year-old children with FHR of SZ (FHR-SZ) or FHR of BP (FHR-BP) and PBC children as part of a register-based, prospective cohort-study; The Danish High Risk and Resilience study-VIA 11. STUDY RESULTS: We included 85 (44% female) FHR-SZ, 63 (52% female) FHR-BP and 98 (50% female) PBC in the analyses. Executive control effects, caused by the spatial visuomotor conflict, showed no differences between groups. Bayesian ANOVA of reaction time (RT) variability, quantified by the coefficient of variation (CVRT), revealed a group effect with similarly higher CVRT in FHR-BP and FHR-SZ compared to PBC (BF10 = 6.82). The fMRI analyses revealed no evidence for between-group differences in task-related brain activation. Post hoc analyses excluding children with psychiatric illness yielded same results. CONCLUSION: FHR-SZ and FHR-BP at age 11-12 show intact ability to resolve a spatial visuomotor conflict and neural efficacy. The increased variability in RT may reflect difficulties in maintaining sustained attention. Since variability in RT was independent of existing psychiatric illness, it may reflect a potential endophenotypic marker of risk.

Auditory stimulus-response modeling with a match-mismatch task.

Objective.An auditory stimulus can be related to the brain response that it evokes by a stimulus-response model fit to the data. This offers insight into perceptual processes within the brain and is also of potential use for devices such as brain computer interfaces (BCIs). The quality of the model can be quantified by measuring the fit with a regression problem, or by applying it to a classification task and measuring its performance.Approach.Here we focus on amatch-mismatch(MM) task that entails deciding whether a segment of brain signal matches, via a model, the auditory stimulus that evoked it.Main results. Using these metrics, we describe a range of models of increasing complexity that we compare to methods in the literature, showing state-of-the-art performance. We document in detail one particular implementation, calibrated on a publicly-available database, that can serve as a robust reference to evaluate future developments.Significance.The MM task allows stimulus-response models to be evaluated in the limit of very high model accuracy, making it an attractive alternative to the more commonly used task of auditory attention detection. The MM task does not require class labels, so it is immune to mislabeling, and it is applicable to data recorded in listening scenarios with only one sound source, thus it is cheap to obtain large quantities of training and testing data. Performance metrics from this task, associated with regression accuracy, provide complementary insights into the relation between stimulus and response, as well as information about discriminatory power directly applicable to BCI applications.

A Comparison of Regularization Methods in Forward and Backward Models for Auditory Attention Decoding.

The decoding of selective auditory attention from noninvasive electroencephalogram (EEG) data is of interest in brain computer interface and auditory perception research. The current state-of-the-art approaches for decoding the attentional selection of listeners are based on linear mappings between features of sound streams and EEG responses (forward model), or vice versa (backward model). It has been shown that when the envelope of attended speech and EEG responses are used to derive such mapping functions, the model estimates can be used to discriminate between attended and unattended talkers. However, the predictive/reconstructive performance of the models is dependent on how the model parameters are estimated. There exist a number of model estimation methods that have been published, along with a variety of datasets. It is currently unclear if any of these methods perform better than others, as they have not yet been compared side by side on a single standardized dataset in a controlled fashion. Here, we present a comparative study of the ability of different estimation methods to classify attended speakers from multi-channel EEG data. The performance of the model estimation methods is evaluated using different performance metrics on a set of labeled EEG data from 18 subjects listening to mixtures of two speech streams. We find that when forward models predict the EEG from the attended audio, regularized models do not improve regression or classification accuracies. When backward models decode the attended speech from the EEG, regularization provides higher regression and classification accuracies.