Brain Connectivity & Machine Learning

Prefrontal brain connectivity re-organization after traumatic axonal injury

Ibai Diez, David Drijkoningen, Sebastiano Stramaglia, Paolo Bonifazi, Daniele Marinazzo, Stephan P. Swinnen*, Jesus M. Cortes* (* Equal last-author contribution). Prefrontal brain connectivity re-organization after traumatic axonal injury. 22nd Annual Meeting of the Organization for Human Brain Mapping, OHBM 2016 [pdf]

Background. Traumatic axonal injury damages brain structural connectivity which is later compensated by a re-organization in structural-functional circuits in a form which remains inadequately characterized.
Methods. We enrolled N=14 young participants who had a traumatic axonal injury ranging from moderate to severe (age: 13.14 ± 3.25 years, 6 males, 8 females). N=27 young participants were used as the control group (age: 15.04 ± 2.26 years, 12 males, 15 females). The average age at the time of injury was 10 ± 2.26 years and the time interval between injury occurrence and the imaging session was on average 3.5 years. The study was approved by the KU Leuven Ethics Committee for biomedical research (Principal Investigator: Stephan P. Swinnen).
Large-scale brain networks were obtained after magnetic resonance imaging (MRI) and using a new hierarchical atlas in which regions are functionally coherent and at the same time structurally integrated [1]. A 3T Magnetom Trio MRI scanner (Siemens) with a 12-channel matrix head coil was used. MRI scanning sessions consisted in same-subject triple acquisitions: anatomy (T1), diffusion tensor imaging and resting T2* functional imaging. Whilst the T1 was mainly used for preprocessing and coregistration, diffusion tensor imaging provided structural networks of white-matter between brain areas, and variations in the blood- oxygenation-level-dependent T2* signal provided functional networks.
Results. We found increased functional and structural connectivity in the frontal lobe in TAI patients as compared to controls. In particular, this increase was evident in a cortico-subcortical network composed of prefrontal cortex, anterior cingulate, orbital gyrus, and the caudate nucleus. Patients (but not control subjects) increased the activity of such frontal network in correlation with the activation of two other networks: 1. A subcortical network, including part of the motor network, the basal ganglia, cerebellum, different subcortical structures, frontal inferior regions, the occipital region, the cingulum posterior and the precuneus, and 2. The task-positive network, including regions of the dorsal attention system together with the dorsolateral and ventrolateral prefrontal regions. Furthermore, we also found that the increased activity in frontal regions measured with the point-process analysis (PPA) [2] was correlated with some behavioural indexes, as for instance, with the amount of body sway [3], showing that patients with worse balance performance were the ones with a higher activation in frontal regions.
Conclusions. The increased prefrontal connectivity found in TAI patients (marked with a yellow rectangle in figure 1) may provide the structural scaffold for increased cognitive control of functions as compared to automatic processing. This is consistent with the observation that various motor tasks occur less automatically in TAI and are associated with more cognitive penetration into action control.

[1] Diez I, Bonifazi P, Escudero I, Mateos B, Munoz MA, Stramaglia S, and Cortes JM (2015), ‘A novel brain partition highlights the modular skeleton shared by structure and function’, Scientific Reports, vol. 5, pp. 10532. The atlas can be downloaded at
[2] Tagliazucchi E, Balenzuela P, Fraiman D, and Chialvo DR (2012), ‘Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis’, Frontiers in Physiology, vol. 3, pp. 15
[3] Caeyenberghs K, Leemans A, De Decker C, Heitger M, Drijkoningen D, Vander Linden C, Sunaert S, and Swinnen SP (2012), ‘Brain connectivity and postural control in young traumatic brain injury patients: A diffusion MRI based network analysis’, NeuroImage: Clinical, vol. 1, pp. 20106-20115
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