Perturbation of Macaque Supplementary Motor Area Produces Context-Independent Changes in the Probability of Movement Initiation

HD-tACS over the left frontal aslant tract entrains theta activity associated with speech motor control

Transient disruption or permanent damage to the left Frontal Aslant Tract (FAT) is associated with deficits in speech production. The present study examined the application of theta (4 Hz) high-definition transcranial alternating current stimulation (HD-tACS) over the left SMA and IFG -as a part of FAT- as a potential multisite protocol to modulate neural and behavioral correlates of speech motor control. Twenty-one young adults participated in three counterbalanced sessions in which they received in-phase, anti-phase, and sham theta HD-tACS. In each session, 4 Hz stimulation was applied over the left IFG and SMA, and subsequently EEG data was recorded while participants performed a speech Go/No-Go task. Relative to sham and anti-phase, in-phase HD-tACS significantly improved speech reaction time. Neural data showed an increase in the power of frontal theta activity prior to speech initiation for the in-phase condition compared to sham. Moreover, in-phase stimulation increased the phase synchrony of theta activity between the left central and frontal electrodes. For speech inhibition, the power of theta activity increased following the in-phase condition over frontocentral electrodes. Furthermore, the in-phase condition enhanced the connectivity between the left central and frontal electrodes. Overall findings suggest that in-phase theta HD-tACS of FAT enhanced the neural markers of cognitive control required for motor preparation and inhibition during a speech task and have translational implications.

Neural correlates of Type A personality: Type A personality mediates the association of resting-state brain activity and connectivity with eating disorder symptoms

BACKGROUND

Type A personality (TAP) was characterized by impatience, competitiveness, aggressiveness, and hostility. Higher TAP was proved to be associated with more eating disorder symptoms (EDS). While little is known about the underlying neural substrates of TAP and how TAP is linked to EDS at the neural level.

METHODS

To investigate the neural basis of TAP, we adopted fractional amplitude of low-frequency fluctuations (fALFF) and resting-state functional connectivity (RSFC) via resting-state functional magnetic resonance imaging (rs-fMRI) (N = 1620). Mediation models were examined to explore the relationship between TAP, EDS, and brain activity.

RESULTS

TAP was associated with decreased fALFF in the left middle frontal gyrus (MFG) and increased fALFF in the left precentral gyrus (PreCG). Furthermore, TAP was positively correlated to RSFC between the left MFG and left inferior temporal gyrus (ITG) and between the left PreCG and right middle temporal gyrus (MTG). Mediation analysis showed TAP fully mediated the association of the left MFG activity, MFG-ITG connectivity, and PreCG-MTG connectivity with EDS.

LIMITATIONS

The cross-sectional design of this study precludes us from specifying the causal relationship in the associations we observed.

CONCLUSIONS

Our results suggested spontaneous activity in the left MFG and PreCG is associated with TAP, and even in general sample, people with higher TAP showed more EDS. The present study is the first to investigate the neurobiological underpinnings of TAP in a large sample and further offered new insights into the relation between TAP and EDS from a neural basis perspective.

A Theoretical Framework for Human and Nonhuman Vocal Interaction

Vocal communication is a critical feature of social interaction across species; however, the relation between such behavior in humans and nonhumans remains unclear. To enable comparative investigation of this topic, we review the literature pertinent to interactive language use and identify the superset of cognitive operations involved in generating communicative action. We posit these functions comprise three intersecting multistep pathways: (a) the Content Pathway, which selects the movements constituting a response; (b) the Timing Pathway, which temporally structures responses; and (c) the Affect Pathway, which modulates response parameters according to internal state. These processing streams form the basis of the Convergent Pathways for Interaction framework, which provides a conceptual model for investigating the cognitive and neural computations underlying vocal communication across species.

Toward Precise Localization of Abnormal Brain Activity: 1D CNN on Single Voxel fMRI Time-Series

Functional magnetic resonance imaging (fMRI) is one of the best techniques for precise localization of abnormal brain activity non-invasively. Machine-learning approaches have been widely used in neuroimaging studies; however, few studies have investigated the single-voxel modeling of fMRI data under cognitive tasks. We proposed a hybrid one-dimensional (1D) convolutional neural network (1D-CNN) based on the temporal dynamics of single-voxel fMRI time-series and successfully differentiated two continuous task states, namely, self-initiated (SI) and visually guided (VG) motor tasks. First, 25 activation peaks were identified from the contrast maps of SI and VG tasks in a blocked design. Then, the fMRI time-series of each peak voxel was transformed into a temporal-frequency domain by using continuous wavelet transform across a broader frequency range (0.003–0.313 Hz, with a step of 0.01 Hz). The transformed time-series was inputted into a 1D-CNN model for the binary classification of SI and VG continuous tasks. Compared with the univariate analysis, e.g., amplitude of low-frequency fluctuation (ALFF) at each frequency band, including, wavelet-ALFF, the 1D-CNN model highly outperformed wavelet-ALFF, with more efficient decoding models [46% of 800 models showing area under the curve (AUC) > 0.61] and higher decoding accuracies (94% of the efficient models), especially on the high-frequency bands (>0.1 Hz). Moreover, our results also demonstrated the advantages of wavelet decompositions over the original fMRI series by showing higher decoding performance on all peak voxels. Overall, this study suggests a great potential of single-voxel analysis using 1D-CNN and wavelet transformation of fMRI series with continuous, naturalistic, steady-state task design or resting-state design. It opens new avenues to precise localization of abnormal brain activity and fMRI-guided precision brain stimulation therapy.

A habenula-insular circuit encodes the willingness to act

The decision that it is worth doing something rather than nothing is a core yet understudied feature of voluntary behaviour. Here we study "willingness to act", the probability of making a response given the context. Human volunteers encountered opportunities to make effortful actions in order to receive rewards, while watching a movie inside a 7 T MRI scanner. Reward and other context features determined willingness-to-act. Activity in the habenula tracked trial-by-trial variation in participants' willingness-to-act. The anterior insula encoded individual environment features that determined this willingness. We identify a multi-layered network in which contextual information is encoded in the anterior insula, converges on the habenula, and is then transmitted to the supplementary motor area, where the decision is made to either act or refrain from acting via the nigrostriatal pathway.

Distinct dynamics of neuronal activity during concurrent motor planning and execution

The smooth conduct of movements requires simultaneous motor planning and execution according to internal goals. So far it remains unknown how such movement plans are modified without interfering with ongoing movements. Previous studies have isolated planning and execution-related neuronal activity by separating behavioral planning and movement periods in time by sensory cues. Here, we separate continuous self-paced motor planning from motor execution statistically, by experimentally minimizing the repetitiveness of the movements. This approach shows that, in the rat sensorimotor cortex, neuronal motor planning processes evolve with slower dynamics than movement-related responses. Fast-evolving neuronal activity precees skilled forelimb movements and is nested within slower dynamics. We capture this effect via high-pass filtering and confirm the results with optogenetic stimulations. The various dynamics combined with adaptation-based high-pass filtering provide a simple principle for separating concurrent motor planning and execution.

Microstimulation of the Premotor Cortex of the Cat Produces Phase-Dependent Changes in Locomotor Activity

To determine the functional organization of premotor areas in the cat pericruciate cortex we applied intracortical microstimulation (ICMS) within multiple cytoarchitectonically identified subregions of areas 4 and 6 in the awake cat, both at rest and during treadmill walking. ICMS in most premotor areas evoked clear twitch responses in the limbs and/or head at rest. During locomotion, these same areas produced phase-dependent modifications of muscle activity. ICMS in the primary motor cortex (area 4γ) produced large phase-dependent responses, mostly restricted to the contralateral forelimb or hindlimb. Stimulation in premotor areas also produced phase-dependent responses that, in some cases, were as large as those evoked from area 4γ. However, responses from premotor areas had more widespread effects on multiple limbs, including the ipsilateral limbs, than did stimulation in 4γ. During locomotion, responses in both forelimb and hindlimb muscles were evoked from cytoarchitectonic areas 4γ, 4δ, 6aα, and 6aγ. However, the prevalence of effects in a given limb varied from one area to another. The results suggest that premotor areas may contribute to the production, modification, and coordination of activity in the limbs during locomotion and may be particularly pertinent during modifications of gait.

Auditory decisions in the supplementary motor area

In human speech and communication across various species, recognizing and categorizing sounds is fundamental for the selection of appropriate behaviors. However, how does the brain decide which action to perform based on sounds? We explored whether the supplementary motor area (SMA), responsible for linking sensory information to motor programs, also accounts for auditory-driven decision making. To this end, we trained two rhesus monkeys to discriminate between numerous naturalistic sounds and words learned as target (T) or non-target (nT) categories. We found that the SMA at single and population neuronal levels perform decision-related computations that transition from auditory to movement representations in this task. Moreover, we demonstrated that the neural population is organized orthogonally during the auditory and the movement periods, implying that the SMA performs different computations. In conclusion, our results suggest that the SMA integrates acoustic information in order to form categorical signals that drive behavior.

Entrained neuronal activity to periodic visual stimuli in the primate striatum compared with the cerebellum

Rhythmic events recruit neuronal activity in the basal ganglia and cerebellum, but their roles remain elusive. In monkeys attempting to detect a single omission of isochronous visual stimulus, we found that neurons in the caudate nucleus showed increased activity for each stimulus in sequence, while those in the cerebellar dentate nucleus showed decreased activity. Firing modulation in the majority of caudate neurons and all cerebellar neurons was proportional to the stimulus interval, but a quarter of caudate neurons displayed a clear duration tuning. Furthermore, the time course of population activity in the cerebellum well predicted stimulus timing, whereas that in the caudate reflected stochastic variation of response latency. Electrical stimulation to the respective recording sites confirmed a causal role in the detection of stimulus omission. These results suggest that striatal neurons might represent periodic response preparation while cerebellar nuclear neurons may play a role in temporal prediction of periodic events.