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Motor performance declines with normal aging. Previous neuroimaging work revealed aging-related general increases in neural activity, especially in the prefrontal and pre-motor areas, associated with a loss of hemispheric lateralization. However, the functional mechanisms underlying these changes and their relation to aging-associated motor decline to date remain elusive. To further elucidate the neural processes underlying aging-related motor decline, we recorded EEG from younger and older subjects while they performed a finger-tapping task. As a measure of synchronization between motor areas, we computed the inter-regional phase-locking value which reflects functional connectivity between distinct neural populations. Behavioral data revealed increased movement times in older subjects. EEG data showed that phase locking in the δ-θ frequencies is a general, age-independent phenomenon underlying the execution of simple finger movements. In stark contrast, the extent of synchronization between motor areas significantly differed dependent upon age of subjects: multiple additional intra- and inter-hemispheric connections were observed in older subjects. Our data shed light upon the results of previous neuroimaging studies showing aging-related increases in neural activation. In particular, data suggest that the observed aging-dependent substantial intra- and inter-hemispheric reorganization of connectivity between the corresponding motor areas underlies the previously reported loss of lateralization in older subjects. The changes observed are likely to represent compensatory mechanisms aiming at preserved task performance in older subjects.

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With increasing age cognitive performance slows down. This includes cognitive processes essential for motor performance. Additionally, performance of motor tasks becomes less accurate. The objective of the present study was to identify general neural correlates underlying age-related behavioral slowing and the reduction in motor task accuracy. To this end, we continuously recorded EEG activity from 18 younger and 24 older right-handed healthy participants while they were performing a simple finger tapping task. We analyzed the EEG records with respect to local changes in amplitude (power spectrum) as well as phase locking between the two age groups. We found differences between younger and older subjects in the amplitude of post-movement synchronization in the β band of the sensory-motor and medial prefrontal cortex (mPFC). This post-movement β amplitude was significantly reduced in older subjects. Moreover, it positively correlated with the accuracy with which subjects performed the motor task at the electrode FCz, which detects activity of the mPFC and the supplementary motor area. In contrast, we found no correlation between the accurate timing of local neural activity, i.e. phase locking in the δ-θ frequency band, with the reaction and movement time or the accuracy with which the motor task was performed. Our results show that only post-movement β amplitude and not δ-θ phase locking is involved in the control of movement accuracy. The decreased post-movement β amplitude in the mPFC of older subjects hints at an impaired deactivation of this area, which may affect the cognitive control of stimulus-induced motor tasks and thereby motor output.

Cognitive performance slows down with increasing age. This includes cognitive processes that are essential for the performance of a motor act, such as the slowing down in response to an external stimulus. The objective of this study was to identify aging‐associated functional changes in the brain networks that are involved in the transformation of external stimuli into motor action. To investigate this topic, we employed dynamic graphs based on phase‐locking of Electroencephalography signals recorded from healthy younger and older subjects while performing a simple visually‐cued finger‐tapping task. The network analysis yielded specific age‐related network structures varying in time in the low frequencies (2–7 Hz), which are closely connected to stimulus processing, movement initiation and execution in both age groups. The networks in older subjects, however, contained several additional, particularly interhemispheric, connections and showed an overall increased coupling density. Cluster analyses revealed reduced variability of the subnetworks in older subjects, particularly during movement preparation. In younger subjects, occipital, parietal, sensorimotor and central regions were—temporally arranged in this order—heavily involved in hub nodes. Whereas in older subjects, a hub in frontal regions preceded the noticeably delayed occurrence of sensorimotor hubs, indicating different neural information processing in older subjects. All observed changes in brain network organization, which are based on neural synchronization in the low frequencies, provide a possible neural mechanism underlying previous fMRI data, which report an overactivation, especially in the prefrontal and pre‐motor areas, associated with a loss of hemispheric lateralization in older subjects.

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The flexibility in adjusting the decision strategy from trial to trial is a prerequisite for learning in a probabilistic environment. Corresponding neural underpinnings remain largely unexplored. In the present study, 28 male humans were engaged in an associative learning task, in which they had to learn the changing probabilistic strengths of tactile sample stimuli. Combining functional magnetic resonance imaging with computational modeling, we show that an unchanged decision strategy over successively presented trials related to weakened functional connectivity between ventralmedial prefrontal cortex (vmPFC) and left secondary somatosensory cortex. The weaker the connection strength, the faster participants indicated their choice. If the decision strategy remained unchanged, participant’s decision confidence (i.e., prior belief) was related to functional connectivity between vmPFC and right pulvinar. While adjusting the decision strategy, we instead found confidence-related connections between left orbitofrontal cortex and left thalamic mediodorsal nucleus. The stronger the participant’s prior belief, the weaker the connection strengths. Together, these findings suggest that distinct thalamo–prefrontal pathways encode the confidence in keeping or changing the decision strategy during probabilistic learning. Low confidence in the decision strategy demands more thalamo–prefrontal processing resources, which is in-line with the theoretical accounts of the free-energy principle.

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.