Disruption of functional organization within the primary motor cortex in children with autism

Abstract

Accumulating evidence suggests that motor impairments are prevalent in autism spectrum disorder (ASD), relate to the social and communicative deficits at the core of the diagnosis and may reflect abnormal connectivity within brain networks underlying motor control and learning. Parcellation of resting-state functional connectivity data using spectral clustering approaches has been shown to be an effective means of visualizing functional organization within the brain but has most commonly been applied to explorations of normal brain function. This article presents a parcellation of a key area of the motor network, the primary motor cortex (M1), a key area of the motor control network, in adults, typically developing (TD) children and children with ASD and introduces methods for selecting the number of parcels, matching parcels across groups and testing group differences. The parcellation is based solely on patterns of connectivity between individual M1 voxels and all voxels outside of M1, and within all groups, a gross dorsomedial to ventrolateral organization emerged within M1 which was left–right symmetric. Although this gross organizational scheme was present in both groups of children, statistically significant group differences in the size and segregation of M1 parcels within regions of the motor homunculus corresponding to the upper and lower limbs were observed. Qualitative comparison of the M1 parcellation for children with ASD with that of younger and older TD children suggests that these organizational differences, with a lack of differentiation between lower limb/trunk regions and upper limb/hand regions, may be due, at least in part, to a delay in functional specialization within the motor cortex.

What can autism teach us about the role of sensorimotor systems in higher cognition? New clues from studies on language, action semantics, and abstract emotional concept processing

“…(Postcentral gyrus, adjacent to this, contains a similar sensory homunculus processing information from each of these regions). Nebel, Joel et al, 2014 examined the functional parcellation of M1 in ASC and found reductions in functional segregation between the upper and lower limbs; abnormalities were also seen in the region linked with dexterous, complex movements of hand, arm and shoulder. The authors suggest that functional organization of M1 was immature in their child participants (a conclusion also posited by Carper et al (2015), who interpret functional hyperconnectivity in the motor system to reflect reduced functional segregation).…”

Big Data and Neuroimaging

“…Third, functional parcellations can be used to define functionally meaningful brain network nodes for graph analysis [41]. Finally, features of the functional parcellations themselves, such as the number or size of parcels in a pre-defined region of interest (ROI), can be studied to better understand differences in brain connectivity and organization across individuals and populations [42]. …”

“…Such parcellations have the potential to increase understanding of group-level analyses by incorporating subject-level covariates. For instance, previous research has established differences in group-level parcellations of the motor cortex in children with autism spectrum disorder (ASD) and typically developing (TD) children [42]. Further analysis incorporating subject-level parcellations and covariates has the potential to shed light on the drivers of this effect, such as a developmental delay in children with ASD.…”

Advances in Spinal Functional Magnetic Resonance Imaging in the Healthy and Injured Spinal Cords

“…This has strong clinical appeal, as it allows the use of an identical protocol for individuals not only in various stages of diseases, but also in various stages of interventions – regardless of their degree of cognitive or physical limitations. 2) capacity to observe functional organization within major intrinsic neural networks – through the use of various advanced data analysis methods, several groups have shown that it is possible to localize functionally relevant subdivisions of anatomically defined regions based on the rsfMRI derived measures (6,36–38). 3) high sensitivity to functional changes in CNS – previous studies have also shown that rsfMRI-derived output measures are sensitive to changes in function, by demonstrating that functional reorganization in CNS could be observed after as little as one week of intervention (39–41).…”

“…However, characterizing and monitoring the plastic neurological changes of the spinal cords in individuals with SCI using ISNCSCI is challenging, because while ISNCSCI is excellent in describing the neurological level of SCI, its ability to describe the degree of functional loss, and therefore the sensitivity to changes in function, is limited (4–6). Other neurological assessment measures are also available, such as neurophysiological tests (e.g., transcranial magnetic stimulation and somatosensory evoked potentials tests (7)), capacity measures (e.g., 10 meter walk test (10MWT) (8)), as well as performance measures (e.g., Spinal Cord Independence Measures (SCIM) (9)).…”