Grasping Kinematics from the Perspective of the Individual Digits: A Modelling Study

Grasping is a prototype of human motor coordination. Nevertheless, it is not known what determines the typical movement patterns of grasping. One way to approach this issue is by building models. We developed a model based on the movements of the individual digits. In our model the following objectives were taken into account for each digit: move smoothly to the preselected goal position on the object without hitting other surfaces, arrive at about the same time as the other digit and never move too far from the other digit. These objectives were implemented by regarding the tips of the digits as point masses with a spring between them, each attracted to its goal position and repelled from objects' surfaces. Their movements were damped. Using a single set of parameters, our model can reproduce a wider variety of experimental findings than any previous model of grasping. Apart from reproducing known effects (even the angles under which digits approach trapezoidal objects' surfaces, which no other model can explain), our model predicted that the increase in maximum grip aperture with object size should be greater for blocks than for cylinders. A survey of the literature shows that this is indeed how humans behave. The model can also adequately predict how single digit pointing movements are made. This supports the idea that grasping kinematics follow from the movements of the individual digits

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. The proposed technique employs multi-spectrum networks to characterize the complex yet subtle blood oxygenation level dependent (BOLD) signal changes caused by pathological attacks. The utilization of multi-spectrum networks in identifying MCI individuals is motivated by the inherent frequency-specific properties of BOLD spectrum. It is believed that frequency specific information extracted from different spectra may delineate the complex yet subtle variations of BOLD signals more effectively. In the proposed technique, regional mean time series of each region-of-interest (ROI) is band-pass filtered ( Hz) before it is decomposed into five frequency sub-bands. Five connectivity networks are constructed, one from each frequency sub-band. Clustering coefficient of each ROI in relation to the other ROIs are extracted as features for classification. Classification accuracy was evaluated via leave-one-out cross-validation to ensure generalization of performance. The classification accuracy obtained by this approach is 86.5%, which is an increase of at least 18.9% from the conventional full-spectrum methods. A cross-validation estimation of the generalization performance shows an area of 0.863 under the receiver operating characteristic (ROC) curve, indicating good diagnostic power. It was also found that, based on the selected features, portions of the prefrontal cortex, orbitofrontal cortex, temporal lobe, and parietal lobe regions provided the most discriminant information for classification, in line with results reported in previous studies. Analysis on individual frequency sub-bands demonstrated that different sub-bands contribute differently to classification, providing extra evidence regarding frequency-specific distribution of BOLD signals. Our MCI classification framework, which allows accurate early detection of functional brain abnormalities, makes an important positive contribution to the treatment management of potential AD patients

Neuroimaging in Dementia

Dementia is a common illness with an incidence that is rising as the aged population increases. There are a number of neurodegenerative diseases that cause dementia, including Alzheimer’s disease, dementia with Lewy bodies, and frontotemporal dementia, which is subdivided into the behavioral variant, the semantic variant, and nonfluent variant. Numerous other neurodegenerative illnesses have an associated dementia, including corticobasal degeneration, Creutzfeldt–Jakob disease, Huntington’s disease, progressive supranuclear palsy, multiple system atrophy, Parkinson’s disease dementia, and amyotrophic lateral sclerosis. Vascular dementia and AIDS dementia are secondary dementias. Diagnostic criteria have relied on a constellation of symptoms, but the definite diagnosis remains a pathologic one. As treatments become available and target specific molecular abnormalities, differentiating amongst the various primary dementias early on becomes essential. The role of imaging in dementia has traditionally been directed at ruling out treatable and reversible etiologies and not to use imaging to better understand the pathophysiology of the different dementias. Different brain imaging techniques allow the examination of the structure, biochemistry, metabolic state, and functional capacity of the brain. All of the major neurodegenerative disorders have relatively specific imaging findings that can be identified. New imaging techniques carry the hope of revolutionizing the diagnosis of neurodegenerative disease so as to obtain a complete molecular, structural, and metabolic characterization, which could be used to improve diagnosis and to stage each patient and follow disease progression and response to treatment. Structural and functional imaging modalities contribute to the diagnosis and understanding of the different dementias

Lateral frontal cortex volume reduction in Tourette syndrome revealed by VBM

<p>Abstract</p> <p>Background</p> <p>Structural changes have been found predominantly in the frontal cortex and in the striatum in children and adolescents with Gilles de la Tourette syndrome (GTS). The influence of comorbid symptomatology is unclear. Here we sought to address the question of gray matter abnormalities in GTS patients <it>with </it>co-morbid obsessive-compulsive disorder (OCD) and/or attention deficit hyperactivity disorder (ADHD) using voxel-based morphometry (VBM) in twenty-nine adult actually unmedicated GTS patients and twenty-five healthy control subjects.</p> <p>Results</p> <p>In GTS we detected a cluster of decreased gray matter volume in the left inferior frontal gyrus (IFG), but no regions demonstrating volume increases. By comparing subgroups of GTS with comorbid ADHD to the subgroup with comorbid OCD, we found a left-sided amygdalar volume increase.</p> <p>Conclusions</p> <p>From our results it is suggested that the left IFG may constitute a common underlying structural correlate of GTS with co-morbid OCD/ADHD. A volume reduction in this brain region that has been previously identified as a key region in OCD and was associated with the active inhibition of attentional processes may reflect the failure to control behavior. Amygdala volume increase is discussed on the background of a linkage of this structure with ADHD symptomatology. Correlations with clinical data revealed gray matter volume changes in specific brain areas that have been described in these conditions each.</p

The cognitive neuroscience of prehension: recent developments

Prehension, the capacity to reach and grasp, is the key behavior that allows humans to change their environment. It continues to serve as a remarkable experimental test case for probing the cognitive architecture of goal-oriented action. This review focuses on recent experimental evidence that enhances or modifies how we might conceptualize the neural substrates of prehension. Emphasis is placed on studies that consider how precision grasps are selected and transformed into motor commands. Then, the mechanisms that extract action relevant information from vision and touch are considered. These include consideration of how parallel perceptual networks within parietal cortex, along with the ventral stream, are connected and share information to achieve common motor goals. On-line control of grasping action is discussed within a state estimation framework. The review ends with a consideration about how prehension fits within larger action repertoires that solve more complex goals and the possible cortical architectures needed to organize these actions