MRS-assessed brain GABA modulation in response to task performance and learning.

Gamma-aminobutyric acid (GABA), the most important inhibitory neurotransmitter in the human brain, has long been considered essential in human behavior in general and learning in particular. GABA concentration can be quantified using magnetic resonance spectroscopy (MRS). Using this technique, numerous studies have reported associations between baseline GABA levels and various human behaviors. However, regional GABA concentration is not fixed and may exhibit rapid modulation as a function of environmental factors. Hence, quantification of GABA levels at several time points during the performance of tasks can provide insights into the dynamics of GABA levels in distinct brain regions. This review reports on findings from studies using repeated measures (n = 41) examining the dynamic modulation of GABA levels in humans in response to various interventions in the perceptual, motor, and cognitive domains to explore associations between GABA modulation and human behavior. GABA levels in a specific brain area may increase or decrease during task performance or as a function of learning, depending on its precise involvement in the process under investigation. Here, we summarize the available evidence and derive two overarching hypotheses regarding the role of GABA modulation in performance and learning. Firstly, training-induced increases in GABA levels appear to be associated with an improved ability to differentiate minor perceptual differences during perceptual learning. This observation gives rise to the 'GABA increase for better neural distinctiveness hypothesis'. Secondly, converging evidence suggests that reducing GABA levels may play a beneficial role in effectively filtering perceptual noise, enhancing motor learning, and improving performance in visuomotor tasks. Additionally, some studies suggest that the reduction of GABA levels is related to better working memory and successful reinforcement learning. These observations inspire the 'GABA decrease to boost learning hypothesis', which states that decreasing neural inhibition through a reduction of GABA in dedicated brain areas facilitates human learning. Additionally, modulation of GABA levels is also observed after short-term physical exercise. Future work should elucidate which specific circumstances induce robust GABA modulation to enhance neuroplasticity and boost performance.

Association between Inhibitory-Excitatory Balance and Brain Activity Response during Cognitive Flexibility in Young and Older Individuals.

Cognitive flexibility represents the capacity to switch among different mental schemes, providing an adaptive advantage to a changing environment. The neural underpinnings of this executive function have been deeply studied in humans through fMRI, showing that the left inferior frontal cortex (IFC) and the left inferior parietal lobule (IPL) are crucial. Here, we investigated the inhibitory-excitatory balance in these regions by means of γ-aminobutyric acid (GABA+) and glutamate + glutamine (Glx), measured with magnetic resonance spectroscopy, during a cognitive flexibility task and its relationship with the performance level and the local task-induced blood oxygenation level-dependent (BOLD) response in 40 young (18-35 years; 26 female) and 40 older (18-35 years; 21 female) human adults. As the IFC and the IPL are richly connected regions, we also examined whole-brain effects associated with their local metabolic activity. Results did not show absolute metabolic modulations associated with flexibility performance, but the performance level was related to the direction of metabolic modulation in the IPL with opposite patterns in young and older individuals. The individual inhibitory-excitatory balance modulation showed an inverse relationship with the local BOLD response in the IPL. Finally, the modulation of inhibitory-excitatory balance in IPL was related to whole-brain effects only in older individuals. These findings show disparities in the metabolic mechanisms underlying cognitive flexibility in young and older adults and their association with the performance level and BOLD response. Such metabolic differences are likely to play a role in executive functioning during aging and specifically in cognitive flexibility.

Combining muscle-computer interface guided training with bihemispheric tDCS improves upper limb function in patients with chronic stroke.

Transcranial direct current stimulation (tDCS) may facilitate neuroplasticity but with a limited effect when administered while patients with stroke are at rest. Muscle-computer interface (MCI) training is a promising approach for training patients with stroke even if they cannot produce overt movements. However, using tDCS to enhance MCI training has not been investigated. We combined bihemispheric tDCS with MCI training of the paretic wrist and examined the effect of this intervention in patients with chronic stroke. A crossover, double-blind, randomized trial was conducted. Twenty-six patients with chronic stroke performed MCI wrist training for three consecutive days at home while receiving either real tDCS or sham tDCS in counterbalanced order and separated by at least 8 mo. The primary outcome measure was the Fugl-Meyer Assessment Upper Extremity Scale (FMA-UE) that was measured 1 wk before training, on the first training day, on the last training day, and 1 wk after training. There was neither a significant difference in the baseline FMA-UE score between groups nor between intervention periods. Patients improved 3.9 ± 0.6 points in FMA-UE score when receiving real tDCS, and 1.0 ± 0.7 points when receiving sham tDCS (P = 0.003). In addition, patients also showed continuous improvement in their motor control of the MCI tasks over the training days. Our study showed that the training paradigm could lead to functional improvement in patients with chronic stroke. We argue that appropriate MCI training in combination with bihemispheric tDCS could be a useful adjuvant for neurorehabilitation in patients with stroke.NEW & NOTEWORTHY Bihemispheric tDCS combined with a novel MCI training for motor control of wrist extensor can improve upper limb function especially a training-specific effect on the wrist movement in patients with chronic stroke. The training regimen can be personalized with adjustments made daily to accommodate the functional change throughout the intervention. This demonstrates that bihemispheric tDCS with MCI training could complement conventional poststroke neurorehabilitation.

Neural correlates of transfer of learning in motor coordination tasks: role of inhibitory and excitatory neurometabolites.

We aimed to investigate transfer of learning, whereby previously acquired skills impact new task learning. While it has been debated whether such transfer may yield positive, negative, or no effects on performance, very little is known about the underlying neural mechanisms, especially concerning the role of inhibitory (GABA) and excitatory (Glu) (measured as Glu + glutamine (Glx)) neurometabolites, as measured by magnetic resonance spectroscopy (MRS). Participants practiced a bimanual coordination task across four days. The Experimental group trained a task variant with the right hand moving faster than the left (Task A) for three days and then switched to the opposite variant (Task B) on Day4. The control group trained Task B across four days. MRS data were collected before, during, and after task performance on Day4 in the somatosensory (S1) and visual (MT/V5) cortex. Results showed that both groups improved performance consistently across three days. On Day4, the Experimental group experienced performance decline due to negative task transfer while the control group continuously improved. GABA and Glx concentrations obtained during task performance showed no significant group-level changes. However, individual Glx levels during task performance correlated with better (less negative) transfer performance. These findings provide a first window into the neurochemical mechanisms underlying task transfer.

Probing intrahemispheric interactions with a novel dual-site TMS setup.

OBJECTIVE: Using dual-site transcranial magnetic stimulation (dsTMS), the effective connectivity between the primary motor cortex (M1) and adjacent brain areas such as the dorsal premotor cortex (PMd) can be investigated. However, stimulating two brain regions in close proximity (e.g., ±2.3 cm for intrahemispheric PMd-M1) is subject to considerable spatial restrictions that potentially can be overcome by combining two standard figure-of-eight coils in a novel dsTMS setup. METHODS: After a technical evaluation of its induced electric fields, the dsTMS setup was tested in vivo (n = 23) by applying a short-interval intracortical inhibition (SICI) protocol. Additionally, the intrahemispheric PMd-M1 interaction was probed. E-field modelling was performed using SimNIBS. RESULTS: The technical evaluation yielded no major alterations of the induced electric fields due to coil overlap. In vivo, the setup reliably elicited SICI. Investigating intrahemispheric PMd-M1 interactions was feasible (inter-stimulus interval 6 ms), resulting in modulation of M1 output. CONCLUSIONS: The presented dsTMS setup provides a novel way to stimulate two adjacent brain regions with fewer technical and spatial limitations than previous attempts. SIGNIFICANCE: This dsTMS setup enables more accurate and repeatable targeting of brain regions in close proximity and can facilitate innovation in the field of effective connectivity.

Targeted memory reactivation during post-learning sleep does not enhance motor memory consolidation in older adults.

Targeted memory reactivation (TMR) during sleep enhances memory consolidation in young adults by modulating electrophysiological markers of neuroplasticity. Interestingly, older adults exhibit deficits in motor memory consolidation, an impairment that has been linked to age-related degradations in the same sleep features sensitive to TMR. We hypothesised that TMR would enhance consolidation in older adults via the modulation of these markers. A total of 17 older participants were trained on a motor task involving two auditory-cued sequences. During a post-learning nap, two auditory cues were played: one associated to a learned (i.e., reactivated) sequence and one control. Performance during two delayed re-tests did not differ between reactivated and non-reactivated sequences. Moreover, both associated and control sounds modulated brain responses, yet there were no consistent differences between the auditory cue types. Our results collectively demonstrate that older adults do not benefit from specific reactivation of a motor memory trace by an associated auditory cue during post-learning sleep. Based on previous research, it is possible that auditory stimulation during post-learning sleep could have boosted motor memory consolidation in a non-specific manner.

Frequency-dependent connectivity in large-scale resting-state brain networks during sleep.

Functional connectivity (FC) during sleep has been shown to break down as non-rapid eye movement (NREM) sleep deepens before returning to a state closer to wakefulness during rapid eye movement (REM) sleep. However, the specific spatial and temporal signatures of these fluctuations in connectivity patterns remain poorly understood. This study aimed to investigate how frequency-dependent network-level FC fluctuates during nocturnal sleep in healthy young adults using high-density electroencephalography (hdEEG). Specifically, we examined source-localized FC in resting-state networks during NREM2, NREM3 and REM sleep (sleep stages scored using a semi-automatic procedure) in the first three sleep cycles of 29 participants. Our results showed that FC within and between all resting-state networks decreased from NREM2 to NREM3 sleep in multiple frequency bands and all sleep cycles. The data also highlighted a complex modulation of connectivity patterns during the transition to REM sleep whereby delta and sigma bands hosted a persistence of the connectivity breakdown in all networks. In contrast, a reconnection occurred in the default mode and the attentional networks in frequency bands characterizing their organization during wake (i.e., alpha and beta bands, respectively). Finally, all network pairs (except the visual network) showed higher gamma-band FC during REM sleep in cycle three compared to earlier sleep cycles. Altogether, our results unravel the spatial and temporal characteristics of the well-known breakdown in connectivity observed as NREM sleep deepens. They also illustrate a complex pattern of connectivity during REM sleep that is consistent with network- and frequency-specific breakdown and reconnection processes.

Baseline GABA+ levels in areas associated with sensorimotor control predict initial and long-term motor learning progress.

Synaptic plasticity relies on the balance between excitation and inhibition in the brain. As the primary inhibitory and excitatory neurotransmitters, gamma-aminobutyric acid (GABA) and glutamate (Glu), play critical roles in synaptic plasticity and learning. However, the role of these neurometabolites in motor learning is still unclear. Furthermore, it remains to be investigated which neurometabolite levels from the regions composing the sensorimotor network predict future learning outcome. Here, we studied the role of baseline neurometabolite levels in four task-related brain areas during different stages of motor skill learning under two different feedback (FB) conditions. Fifty-one healthy participants were trained on a bimanual motor task over 5 days while receiving either concurrent augmented visual FB (CA-VFB group, N = 25) or terminal intrinsic visual FB (TA-VFB group, N = 26) of their performance. Additionally, MRS-measured baseline GABA+ (GABA + macromolecules) and Glx (Glu + glutamine) levels were measured in the primary motor cortex (M1), primary somatosensory cortex (S1), dorsolateral prefrontal cortex (DLPFC), and medial temporal cortex (MT/V5). Behaviorally, our results revealed that the CA-VFB group outperformed the TA-VFB group during task performance in the presence of augmented VFB, while the TA-VFB group outperformed the CA-VFB group in the absence of augmented FB. Moreover, baseline M1 GABA+ levels positively predicted and DLPFC GABA+ levels negatively predicted both initial and long-term motor learning progress in the TA-VFB group. In contrast, baseline S1 GABA+ levels positively predicted initial and long-term motor learning progress in the CA-VFB group. Glx levels did not predict learning progress. Together, these findings suggest that baseline GABA+ levels predict motor learning capability, yet depending on the FB training conditions afforded to the participants.

White matter and neurochemical mechanisms underlying age-related differences in motor processing speed.

Aging is associated with changes in the central nervous system and leads to reduced life quality. Here, we investigated the age-related differences in the CNS underlying motor performance deficits using magnetic resonance spectroscopy and diffusion MRI. MRS measured N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr) concentrations in the sensorimotor and occipital cortex, whereas dMRI quantified apparent fiber density (FD) in the same voxels to evaluate white matter microstructural organization. We found that aging was associated with increased reaction time and reduced FD and NAA concentration in the sensorimotor voxel. Both FD and NAA mediated the association between age and reaction time. The NAA concentration was found to mediate the association between age and FD in the sensorimotor voxel. We propose that the age-related decrease in NAA concentration may result in reduced axonal fiber density in the sensorimotor cortex which may ultimately account for the response slowness of older participants.

Asymmetric effects of different training-testing mismatch types on myoelectric regression via deep learning.

This paper investigates how predictions of a convolutional neural network (CNN) suited for myoelectric simultaneous and proportional control (SPC) are affected when training and testing conditions differ. We used a dataset composed of electromyogram (EMG) signals and joint angular accelerations measured from volunteers drawing a star. This task was repeated multiple times using different combinations of motion amplitude and frequency. CNNs were trained with data from a given combination and tested under different combinations. Predictions were compared between situations in which training and testing conditions matched versus when there was a training-testing mismatch. Changes in predictions were assessed through three metrics: normalized root mean squared error (NRMSE), correlation, and slope of the linear regression between targets and predictions. We found that predictive performance declined differently depending on whether the confounding factors (amplitude and frequency) increased or decreased between training and testing. Correlations dropped as the factors decreased, whereas slopes deteriorated when factors increased. NRMSEs worsened when factors increased or decreased, with more accentuated deterioration for increasing factors. We argue that worse correlations could be related to differences in EMG signal-to-ratio (SNR) between training and testing, which affected the noise robustness of the CNNs' learned internal features. Slope deterioration could be a result of the networks' inability to predict accelerations outside the range seen during training. These two mechanisms may also asymmetrically increase NRMSE. Finally, our findings open further possibilities to develop strategies to mitigate the negative impact of confounding factor variability on myoelectric SPC devices.

Neurological soft signs are associated with reduced medial-lateral postural control in adolescent athletes.

INTRODUCTION: Neurological soft signs (NSS) are minor deviations from the norm in motor performance that are commonly assessed using neurological examinations. NSS may be of clinical relevance for evaluating the developmental status of adolescents. Here we investigate whether quantitative force plate measures may add relevant information to observer-based neurological examinations. METHODS: Male adolescent athletes (n = 141) aged 13-16 years from three European sites underwent a neurological examination including 28 tests grouped into six functional clusters. The performance of tests and functional clusters was rated as optimal/non-optimal resulting in NSS+/NSS- groups and a continuous total NSS score. Participants performed a postural control task on a Balance Tracking System measured as path length, root mean square and sway area. ANCOVAs were applied to test for group differences in postural control between the NSS+ and NSS- group, and between optimal/non-optimal performance on a cluster- and test-level. Moreover, we tested for correlations between the total NSS score and postural control variables. RESULTS: There was no significant overall difference between the NSS+ and NSS- group in postural control. However, non-optimal performing participants in the diadochokinesis test swayed significantly more in the medial-lateral direction than optimal performing participants. Moreover, a lower total NSS score was associated with reduced postural control in the medial-lateral direction. CONCLUSION: Our findings demonstrate that NSS are related to postural control in adolescent athletes. Thus, force plate measures may add a quantitative, objective measurement of postural control to observer-based qualitative assessments, and thus, may complement clinical testing.

Dual-site TMS as a tool to probe effective interactions within the motor network: a review.

Dual-site transcranial magnetic stimulation (ds-TMS) is well suited to investigate the causal effect of distant brain regions on the primary motor cortex, both at rest and during motor performance and learning. However, given the broad set of stimulation parameters, clarity about which parameters are most effective for identifying particular interactions is lacking. Here, evidence describing inter- and intra-hemispheric interactions during rest and in the context of motor tasks is reviewed. Our aims are threefold: (1) provide a detailed overview of ds-TMS literature regarding inter- and intra-hemispheric connectivity; (2) describe the applicability and contributions of these interactions to motor control, and; (3) discuss the practical implications and future directions. Of the 3659 studies screened, 109 were included and discussed. Overall, there is remarkable variability in the experimental context for assessing ds-TMS interactions, as well as in the use and reporting of stimulation parameters, hindering a quantitative comparison of results across studies. Further studies examining ds-TMS interactions in a systematic manner, and in which all critical parameters are carefully reported, are needed.

Neurological soft signs in adolescents are associated with brain structure.

Neurological soft signs (NSS) are minor deviations in motor performance. During childhood and adolescence, NSS are examined for functional motor phenotyping to describe development, to screen for comorbidities, and to identify developmental vulnerabilities. Here, we investigate underlying brain structure alterations in association with NSS in physically trained adolescents. Male adolescent athletes (n = 136, 13-16 years) underwent a standardized neurological examination including 28 tests grouped into 6 functional clusters. Non-optimal performance in at least 1 cluster was rated as NSS (NSS+ group). Participants underwent T1- and diffusion-weighted magnetic resonance imaging. Cortical volume, thickness, and local gyrification were calculated using Freesurfer. Measures of white matter microstructure (Free-water (FW), FW-corrected fractional anisotropy (FAt), axial and radial diffusivity (ADt, RDt)) were calculated using tract-based spatial statistics. General linear models with age and handedness as covariates were applied to assess differences between NSS+ and NSS- group. We found higher gyrification in a large cluster spanning the left superior frontal and parietal areas, and widespread lower FAt and higher RDt compared with the NSS- group. This study shows that NSS in adolescents are associated with brain structure alterations. Underlying mechanisms may include alterations in synaptic pruning and axon myelination, which are hallmark processes of brain maturation.

Organization of neurochemical interactions in young and older brains as revealed with a network approach: Evidence from proton magnetic resonance spectroscopy (1H-MRS).

Aging is associated with alterations in the brain including structural and metabolic changes. Previous research has focused on neurometabolite level differences associated to age in a variety of brain regions, but the relationship among metabolites across the brain has been much less studied. Investigating these relationships can reveal underlying neurometabolic processes, their interdependency, and their progress throughout the lifespan. Using 1H-MRS, we investigated the relationship among metabolite concentrations of N-acetylaspartate (NAA), creatine (Cr), choline (Cho), myo-Inositol (mIns) and glutamate-glutamine complex (Glx) in seven voxel locations, i.e., bilateral sensorimotor cortex, bilateral striatum, pre-supplementary motor area, right inferior frontal gyrus and occipital cortex. These measurements were performed on 59 human participants divided in two age groups: young adults (YA: 23.2 ± 4.3; 18-34 years) and older adults (OA: 67.5 ± 3.9; 61-74 years). Our results showed age-related differences in NAA, Cho, and mIns across brain regions, suggesting the presence of neurodegeneration and altered gliosis. Moreover, associative patterns among NAA, Cho and Cr were observed across the selected brain regions, which differed between young and older adults. Whereas most of metabolite concentrations were inhomogeneous across different brain regions, Cho levels were shown to be strongly related across brain regions in both age groups. Finally, we found metabolic associations between homologous brain regions (SM1 and striatum) in the OA group, with NAA showing a significant correlation between bilateral sensorimotor cortices (SM1) and mIns levels being correlated between the bilateral striata. We posit that a network perspective provides important insights regarding the potential interactions among neurochemicals underlying metabolic processes at a local and global level and their relationship with aging.

Multimodal and multidomain lesion network mapping enhances prediction of sensorimotor behavior in stroke patients.

Beyond the characteristics of a brain lesion, such as its etiology, size or location, lesion network mapping (LNM) has shown that similar symptoms after a lesion reflects similar dis-connectivity patterns, thereby linking symptoms to brain networks. Here, we extend LNM by using a multimodal strategy, combining functional and structural networks from 1000 healthy participants in the Human Connectome Project. We apply multimodal LNM to a cohort of 54 stroke patients with the aim of predicting sensorimotor behavior, as assessed through a combination of motor and sensory tests. Results are two-fold. First, multimodal LNM reveals that the functional modality contributes more than the structural one in the prediction of sensorimotor behavior. Second, when looking at each modality individually, the performance of the structural networks strongly depended on whether sensorimotor performance was corrected for lesion size, thereby eliminating the effect that larger lesions generally produce more severe sensorimotor impairment. In contrast, functional networks provided similar performance regardless of whether or not the effect of lesion size was removed. Overall, these results support the extension of LNM to its multimodal form, highlighting the synergistic and additive nature of different types of network modalities, and their corresponding influence on behavioral performance after brain injury.

Inhibition, Shifting and Updating: Inter and intra-domain commonalities and differences from an executive functions activation likelihood estimation meta-analysis.

Executive functions are higher-order mental processes that support goal-directed behavior. Among these processes, Inhibition, Updating, and Shifting have been considered core executive domains. In this meta-analysis, we comprehensively investigate the neural networks of these executive domains and we synthesize for the first time the neural convergences and divergences among the most frequently used executive paradigms within those domains. A systematic search yielded 1055 published neuroimaging studies (including 26,191 participants in total). Our study revealed that a fronto-parietal network was shared by the three main domains. Furthermore, we executed conjunction analyses among the paradigms of the same domain to extract the core distinctive components of the main executive domains. This approach showed that Inhibition and Shifting are characterized by a strongly lateralized neural activation in the right and left hemisphere, respectively. In addition, both networks overlapped with the Updating network but not with each other. Remarkably, our study detected heterogeneity among the paradigms from the same domain. More specifically, analysis of Inhibition tasks revealed differing activations for Response Inhibition compared to Interference Control paradigms, suggesting that Inhibition encompasses relatively heterogeneous sub-functions. Shifting analyses revealed a bilateral overlap of the Wisconsin Card Sorting Task with the Updating network, but this pattern was absent for Rule Switching and Dual Task paradigms. Moreover, our Updating meta-analyses revealed the neural signatures associated with the specific modules of the Working Memory model from Baddeley and Hitch. To our knowledge, this is the most comprehensive meta-analysis of executive functions to date. Its paradigm-driven analyses provide a unique contribution to a better understanding of the neural convergences and divergences among executive processes that are relevant for clinical applications, such as cognitive enhancement and neurorehabilitation interventions.

No evidence for a difference in lateralization and distinctiveness level of transcranial magnetic stimulation-derived cortical motor representations over the adult lifespan.

This study aimed to investigate the presence and patterns of age-related differences in TMS-based measures of lateralization and distinctiveness of the cortical motor representations of two different hand muscles. In a sample of seventy-three right-handed healthy participants over the adult lifespan, the first dorsal interosseus (FDI) and abductor digiti minimi (ADM) cortical motor representations of both hemispheres were acquired using transcranial magnetic stimulation (TMS). In addition, dexterity and maximum force levels were measured. Lateralization quotients were calculated for homolog behavioral and TMS measures, whereas the distinctiveness between the FDI and ADM representation within one hemisphere was quantified by the center of gravity (CoG) distance and cosine similarity. The presence and patterns of age-related changes were examined using linear, polynomial, and piecewise linear regression. No age-related differences could be identified for the lateralization quotient of behavior or cortical motor representations of both intrinsic hand muscles. Furthermore, no evidence for a change in the distinctiveness of the FDI and ADM representation with advancing age was found. In conclusion this work showed that lateralization and distinctiveness of cortical motor representations, as determined by means of TMS-based measures, remain stable over the adult lifespan.

High-order functional redundancy in ageing explained via alterations in the connectome in a whole-brain model.

The human brain generates a rich repertoire of spatio-temporal activity patterns, which support a wide variety of motor and cognitive functions. These patterns of activity change with age in a multi-factorial manner. One of these factors is the variations in the brain's connectomics that occurs along the lifespan. However, the precise relationship between high-order functional interactions and connnectomics, as well as their variations with age are largely unknown, in part due to the absence of mechanistic models that can efficiently map brain connnectomics to functional connectivity in aging. To investigate this issue, we have built a neurobiologically-realistic whole-brain computational model using both anatomical and functional MRI data from 161 participants ranging from 10 to 80 years old. We show that the differences in high-order functional interactions between age groups can be largely explained by variations in the connectome. Based on this finding, we propose a simple neurodegeneration model that is representative of normal physiological aging. As such, when applied to connectomes of young participant it reproduces the age-variations that occur in the high-order structure of the functional data. Overall, these results begin to disentangle the mechanisms by which structural changes in the connectome lead to functional differences in the ageing brain. Our model can also serve as a starting point for modeling more complex forms of pathological ageing or cognitive deficits.

Bridging cognition and action: executive functioning mediates the relationship between white matter fiber density and complex motor abilities in older adults.

Aging may be associated with motor decline that is attributed to deteriorating white matter microstructure of the corpus callosum (CC), among other brain-related factors. Similar to motor functioning, executive functioning (EF) typically declines during aging, with age-associated changes in EF likewise being linked to altered white matter connectivity in the CC. Given that both motor and executive functions rely on white matter connectivity via the CC, and that bimanual control is thought to rely on EF, the question arises whether EF can at least party account for the proposed link between CC-connectivity and motor control in older adults. To address this, diffusion magnetic resonance imaging data were obtained from 84 older adults. A fiber-specific approach was used to obtain fiber density (FD), fiber cross-section (FC), and a combination of both metrics in eight transcallosal white matter tracts. Motor control was assessed using a bimanual coordination task. EF was determined by a domain-general latent EF-factor extracted from multiple EF tasks, based on a comprehensive test battery. FD of transcallosal prefrontal fibers was associated with cognitive and motor performance. EF partly accounted for the relationship between FD of prefrontal transcallosal pathways and motor control. Our results underscore the multidimensional interrelations between callosal white matter connectivity (especially in prefrontal brain regions), EF across multiple domains, and motor control in the older population. They also highlight the importance of considering EF when investigating brain-motor behavior associations in older adults.

Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH).

Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.