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Accurate motor performance may depend on the scaling of distinct oscillatory activity within the motor cortex and effective neural communication between the motor cortex and other brain areas. Oscillatory activity within the beta-band (13–30Hz) has been suggested to provide distinct functional roles for attention and sensorimotor control, yet it remains unclear how beta-band and other oscillatory activity within and between cortical regions is coordinated to enhance motor performance. We explore this open issue by simultaneously measuring high-density cortical activity and elbow flexor and extensor neuromuscular activity during ballistic movements, and manipulating error using high and low visual gain across three target distances. Compared with low visual gain, high visual gain decreased movement errors at each distance. Group analyses in 3D source-space revealed increased theta-, alpha-, and beta-band desynchronization of the contralateral motor cortex and medial parietal cortex in high visual gain conditions and this corresponded to reduced movement error. Dynamic causal modeling was used to compute connectivity between motor cortex and parietal cortex. Analyses revealed that gain affected the directionally-specific connectivity across broadband frequencies from parietal to sensorimotor cortex but not from sensorimotor cortex to parietal cortex. These new findings provide support for the interpretation that broad-band oscillations in theta, alpha, and beta frequency bands within sensorimotor and parietal cortex coordinate to facilitate accurate upper limb movement. Our findings establish a link between sensorimotor oscillations in the context of online motor performance in common source space across subjects. Specifically, the extent and distinct role of medial parietal cortex to sensorimotor beta connectivity and local domain broadband activity combine in a time and frequency manner to assist ballistic movements. These findings can serve as a model to examine whether similar source space EEG dynamics exhibit different time-frequency changes in individuals with neurological disorders that cause movement errors. •Cortical activity and connectivity were examined during upper limb movement.•Visual feedback gain led to better motor performance and increased muscle activity.•Increased theta-, alpha-, and beta-band desynchronization at high gain feedback.•Increased parietal-to-motor cortex connectivity in the beta-band at high gain feedback.•Visual gain did not affect motor-to-parietal cortex connectivity.

Previous neuroimaging studies have found hyperactivation in the cerebellum and motor cortex and hypoactivation in the basal ganglia in patients with Parkinson's disease (PD) but the relationship between the two has not been established. This study examined whether cerebellar and motor cortex hyperactivation is a compensatory mechanism for hypoactivation in the basal ganglia or is a pathophysiological response that is related to the signs of the disease. Using a BOLD contrast fMRI paradigm PD patients and healthy controls performed automatic and cognitively controlled thumb pressing movements. Regions of interest analysis quantified the BOLD activation in motor areas, and correlations between the hyperactive and hypoactive regions were performed, along with correlations between the severity of upper limb rigidity and BOLD activation. There were three main findings. First, the putamen, supplementary motor area (SMA) and pre-SMA were hypoactive in PD patients. The left and right cerebellum and the contralateral motor cortex were hyperactive in PD patients. Second, PD patients had a significant negative correlation between the BOLD activation in the ipsilateral cerebellum and the contralateral putamen. The correlation between the putamen and motor cortex was not significant. Third, the BOLD activation in the motor cortex was positively correlated with the severity of upper limb rigidity, but the BOLD activation in the cerebellum was not correlated with rigidity. Further, the activation in the motor cortex was not correlated with upper extremity bradykinesia. These findings provide new evidence supporting the hypothesis that hyperactivation in the ipsilateral cerebellum is a compensatory mechanism for the defective basal ganglia. Our findings also provide the first evidence from neuroimaging that hyperactivation in the contralateral primary motor cortex is not a compensatory response but is directly related to upper limb rigidity.

The mesial premotor cortex (pre-supplementary motor area and supplementary motor area proper), lateral premotor cortex (dorsal premotor cortex and ventral premotor cortex), and primary sensorimotor cortex (primary motor cortex and primary somatosensory cortex) have been identified as key cortical areas for sensorimotor function. However, the three-dimensional (3-D) anatomic boundaries between these regions remain unclear. In order to clarify the locations and boundaries for these six sensorimotor regions, we surveyed 126 articles describing pre-supplementary motor area, supplementary motor area proper, dorsal premotor cortex, ventral premotor cortex, primary motor cortex, and primary somatosensory cortex. Using strict inclusion criteria, we recorded the reported normalized stereotaxic coordinates (Talairach and Tournoux or MNI) from each experiment. We then computed the probability distributions describing the likelihood of activation, and characterized the shape, extent, and area of each sensorimotor region in 3-D. Additionally, we evaluated the nature of the overlap between the six sensorimotor regions. Using the findings from this meta-analysis, along with suggestions and guidelines of previous researchers, we developed the Human Motor Area Template (HMAT) that can be used for ROI analysis. HMAT is available through e-mail from the corresponding author.

Extracellular β-amyloid (Aβ) plaque deposits and inflammatory immune activation are thought to alter various aspects of tissue microstructure, such as extracellular free water, fractional anisotropy and diffusivity, as well as the density and geometric arrangement of axonal processes. Quantifying these microstructural changes in Alzheimer’s disease and related neurodegenerative dementias could serve to monitor or predict disease course. In the present study we used high-field diffusion magnetic resonance imaging (dMRI) to investigate the effects of Aβ and inflammatory interleukin-6 (IL6), alone or in combination, on in vivo tissue microstructure in the TgCRND8 mouse model of Alzheimer’s-type Aβ deposition. TgCRND8 and non-transgenic (nTg) mice expressing brain-targeted IL6 or enhanced glial fibrillary protein (EGFP controls) were scanned at 8 months of age using a 2-shell, 54-gradient direction dMRI sequence at 11.1 T. Images were processed using the diffusion tensor imaging (DTI) model or the neurite orientation dispersion and density imaging (NODDI) model. DTI and NODDI processing in TgCRND8 mice revealed a microstructure pattern in white matter (WM) and hippocampus consistent with radial and longitudinal diffusivity deficits along with an increase in density and geometric complexity of axonal and dendritic processes. This included reduced FA, mean, axial and radial diffusivity, and increased orientation dispersion (ODI) and intracellular volume fraction (ICVF) measured in WM and hippocampus. IL6 produced a ‘protective-like’ effect on WM FA in TgCRND8 mice, observed as an increased FA that counteracted a reduction in FA observed with endogenous Aβ production and accumulation. In addition, we found that ICVF and ODI had an inverse relationship with the functional connectome clustering coefficient. The relationship between NODDI and graph theory metrics suggests that currently unknown microstructure alterations in WM and hippocampus are associated with diminished functional network organization in the brain.

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human brain and provide new insight into the functional neuroanatomy required to predict the motor effector used in a motor control task. [Display omitted]

Integrating visual information for motor output is an essential process of visually-guided motor control. The brainstem is known to be a major center involved in the integration of sensory information for motor output, however, limitations of functional imaging in humans have impaired our knowledge about the individual roles of sub-nuclei within the brainstem. Thus, the bulk of our knowledge surrounding the function of the brainstem is based on anatomical and behavioral studies in non-human primates, cats, and rodents, despite studies demonstrating differences in the organization of visuomotor processing between mammals. fMRI studies in humans have examined activity related to visually-guided motor tasks, however, few have done so while controlling for both force without visual feedback activity and visual stimuli without force activity. Of the studies that have controlled for both conditions, none have reported brainstem activity. Here, we employed a novel fMRI paradigm focused on the brainstem and cerebellum to systematically investigate the hypothesis that the pons and midbrain are critical for the integration of visual information for motor control. Visuomotor activity during visually-guided pinch-grip force was measured while controlling for force without visual feedback activity and visual stimuli without force activity in healthy adults. Using physiological noise correction and multiple task repetitions, we demonstrated that visuomotor activity occurs in the inferior portion of the basilar pons and the midbrain. These findings provide direct evidence in humans that the pons and midbrain support the integration of visual information for motor control. We also determined the effect of physiological noise and task repetitions on the visuomotor signal that will be useful in future studies of neurodegenerative diseases affecting the brainstem.

Early detection of Alzheimer's disease (AD) during the Mild Cognitive Impairment (MCI) could enable effective intervention to slow down the progression of the disease. Computer-aided diagnosis of AD relies on a sufficient amount of biomarker data. When this requirement is not fulfilled, transfer learning can be used to transfer knowledge from a source domain with more amount of labeled data than the desired target domain. In this study, an instance-based transfer learning framework is presented based on the gradient boosting machine (GBM). In GBM, a sequence of base learners is built, and each learner focuses on the errors (residuals) of the previous learner. In our transfer learning version of GBM (TrGB) a weighting mechanism based on the residuals of the base learners is defined for the source instances. Consequently, instances with different distribution than the target data will have a lower impact on the target learner. The target data in this study was obtained from the Mount Sinai dataset which is collected and processed in a collaborative 5-year project at the Mount Sinai medical center. The Alzheimer's Disease neuroimaging initiative (ADNI) dataset was used as the source domain. The experimental results showed that the proposed TrGB algorithm could improve the classification accuracy by 1.5% and 4.5% for CN vs. MCI and multiclass classifications, respectively, compared to the conventional methods. Also, using the TrGB model and transferred knowledge from the CN vs. AD classification of the source domain, the average score of early MCI vs. late MCI classification improved by 5%.

Neuroimaging advances over the past several decades have provided increased understanding of the structural and functional brain changes that occur with Parkinson’s disease (PD). Examination of resting state functional magnetic resonance imaging (rs-fMRI) provides a noninvasive method that focuses on low-frequency spontaneous fluctuations in the blood-oxygenation-level-dependent signal that occurs when an individual is at rest. Several analysis methods have been developed and used to explore how PD affects resting state activity and functional connectivity, and the purpose of this review is to highlight the critical advances made thus far. Some discrepancies in the rs-fMRI and PD literature exist, and we make recommendations for consideration in future studies. The rs-fMRI technique holds promise for investigating brain changes associated with the motor and nonmotor symptoms of PD, and for revealing important variations across large-scale networks of the brain in PD.

Precise motor control requires the ability to scale the parameters of movement. Theta oscillations across the cortex have been associated with changes in memory, attention, and sensorimotor processing. What has proven more elusive is pinpointing the region-specific frequency band oscillations that are associated with specific parameters of movement during the acceleration and deceleration phases. We report a study using 3D analytic techniques for high density electroencephalography that examines electrocortical dynamics while participants produce upper limb movements to different distances at varying rates. During fast ballistic movements, we observed increased theta band activity in the left motor area contralateral to the moving limb during the acceleration phase of the movement, and theta power correlated with the acceleration of movement. In contrast, beta band activity scaled with the type of movement during the deceleration phase near the end of the movement and correlated with movement time. In the ipsilateral motor and somatosensory area, alpha band activity decreased with the type of movement near the end of the movement, and gamma band activity in visual cortex increased with the type of movement near the end of the movement. Our results suggest that humans use distinct lateralized cortical activity for distance and speed dependent arm movements. We provide new evidence that a temporary increase in theta band power relates to movement acceleration and is important during movement execution. Further, the theta power increase is coupled with desychronization of beta band power and alpha band power which are modulated by the task near the end of movement. •We use Measure Projection Analysis for source dynamics in upper arm movements•We show 3D maps with statistical significance for ERSP and ITC measures in upper limb movements•Theta activity from motor cortex is related to movement acceleration•We found that distinct lateralized cortical activity is recruited at the end of movement

Background Restricted repetitive behavior (RRB) is one of two behavioral domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding brain alterations linked to RRB. Methods We utilized neuroimaging data from the National Institute of Mental Health Data Archive to assess basal ganglia and cerebellum structure in a cohort of children and adolescents with ASD compared to typically developing (TD) controls. We evaluated regional gray matter volumes from T1-weighted anatomical scans and assessed diffusion-weighted scans to quantify white matter microstructure with free-water imaging. We also investigated the interaction of biological sex and ASD diagnosis on these measures, and their correlation with clinical scales of RRB. Results Individuals with ASD had significantly lower free-water corrected fractional anisotropy (FA T ) and higher free-water (FW) in cortico-basal ganglia white matter tracts. These microstructural differences did not interact with biological sex. Moreover, both FA T and FW in basal ganglia white matter tracts significantly correlated with measures of RRB. In contrast, we found no significant difference in basal ganglia or cerebellar gray matter volumes. Limitations The basal ganglia and cerebellar regions in this study were selected due to their hypothesized relevance to RRB. Differences between ASD and TD individuals that may occur outside the basal ganglia and cerebellum, and their potential relationship to RRB, were not evaluated. Conclusions These new findings demonstrate that cortico-basal ganglia white matter microstructure is altered in ASD and linked to RRB. FW in cortico-basal ganglia and intra-basal ganglia white matter was more sensitive to group differences in ASD, whereas cortico-basal ganglia FA T was more closely linked to RRB. In contrast, basal ganglia and cerebellar volumes did not differ in ASD. There was no interaction between ASD diagnosis and sex-related differences in brain structure. Future diffusion imaging investigations in ASD may benefit from free-water estimation and correction in order to better understand how white matter is affected in ASD, and how such measures are linked to RRB.