BACKGROUND Functional preoperative planning for resection of intrinsic brain tumors in eloquent areas is still a challenge. Predicting subcortical functional framework is especially difficult. Direct electrical stimulation (DES) is the recommended technique for resection of these lesions. A reliable probabilistic atlas of the critical cortical epicenters and subcortical framework based on DES data was recently published. OBJECTIVE To propose a pipeline for the automated alignment of the corticosubcortical maps of this atlas with T1-weighted MRI. METHODS To test the alignment, we selected 10 patients who underwent resection of brain lesions by using DES. We aligned different cortical and subcortical functional maps to preoperative volumetric T1 MRIs (with/without gadolinium). For each patient we quantified the quality of the alignment, and we calculated the match between the location of the functional sites found at DES and the functional maps of the atlas. RESULTS We found an accurate brain extraction and alignment of the functional maps with both the T1 MRIs of each patient. The matching analysis between functional maps and functional responses collected during surgeries was 88% at cortical and, importantly, 100% at subcortical level, providing a further proof of the correct alignment. CONCLUSION We demonstrated quantitatively and qualitatively the reliability of this tool that may be used for presurgical planning, providing further functional information at the cortical level and a unique probabilistic prevision of distribution of the critical subcortical structures. Finally, this tool offers the chance for multimodal planning through integrating this functional information with other neuroradiological and neurophysiological techniques.
BACKGROUND AND PURPOSE: Polymicrogyria and lissencephaly may be associated with abnormal organization of the undelying white matter tracts that have been rarely investigated so far. Our aim was to characterize white matter tract organization in polymicrogyria and lissencephaly using constrained spherical deconvolution, a multifiber diffusion MR imaging modeling technique for white matter tractography reconstruction. MATERIALS AND METHODS:We retrospectively reviewed 50 patients (mean age, 8.3 6 5.4 years; range, 1.4-21.2 years; 27 males) with different polymicrogyria (n ¼ 42) and lissencephaly (n ¼ 8) subtypes. The fiber direction-encoded color maps and 6 different white matter tracts reconstructed from each patient were visually compared with corresponding images reconstructed from 7 agematched, healthy control WM templates. Each white matter tract was assessed by 2 experienced pediatric neuroradiologists and scored in consensus on the basis of the severity of the structural abnormality, ranging from the white matter tracts being absent to thickened. The results were summarized by different polymicrogyria and lissencephaly subgroups.RESULTS: More abnormal-appearing white matter tracts were identified in patients with lissencephaly compared with those with polymicrogyria (79.2% versus 37.3%). In lissencephaly, structural abnormalities were identified in all studied white matter tracts. In polymicrogyria, the more frequently affected white matter tracts were the cingulum, superior longitudinal fasciculus, inferior longitudinal fasciculus, and optic radiation-posterior corona radiata. The severity of superior longitudinal fasciculus and cingulum abnormalities was associated with the polymicrogyria distribution and extent. A thickened superior fronto-occipital fasciculus was demonstrated in 3 patients. CONCLUSIONS:We demonstrated a range of white matter tract structural abnormalities in patients with polymicrogyria and lissencephaly. The patterns of white matter tract involvement are related to polymicrogyria and lissencephaly subgroups, distribution, and, possibly, their underlying etiologies. ABBREVIATIONS: CG ¼ cingulum; CMV ¼ cytomegalovirus; CSD ¼ constrained spherical deconvolution; DEC ¼ direction-encoded color; dMRI ¼ diffusion MRI; FOD ¼ fiber orientation distribution; HARDI ¼ high angular resolution diffusion imaging; IFOF ¼ inferior fronto-occipital fasciculus; ILF ¼ inferior longitudinal fasciculus; LIS ¼ lissencephaly; MCD ¼ malformation of cortical development; OR-PCR ¼ optic radiation-posterior corona radiata; PMG ¼ polymicrogyria; SBH ¼ subcortical band heterotopia; SFOF ¼ superior fronto-occipital fasciculus/Muratoff bundle; SLF ¼ superior longitudinal fasciculus; WMT ¼ white matter tract
Patients with unilateral spatial neglect (USN) are unable to explore or to report stimuli presented in the left personal and extra‐personal space. USN is usually caused by lesion of the right parietal lobe: nowadays, it is also clear the key role of structural connections (the second and the third branch of the right Superior Longitudinal Fasciculus, respectively, SLF II and III) and functional networks (Dorsal and Ventral Attention Network, respectively, DAN and VAN) in USN. In this multimodal case report, we have merged those structural and functional information derived from a patient with a right parietal lobe tumour and USN before surgery. Functional, structural and neuropsychological data were also collected 6 months after surgery, when the USN was spontaneously recovered. Diffusion metrics and Functional Connectivity (FC) of the right SLF and DAN, before and after surgery, were compared with the same data of a patient with a tumour in a similar location, but without USN, and with a control sample. Results indicate an impairment in the right SLF III and a reduction of FC of the right DAN in patients with USN before surgery compared to controls; after surgery, when USN was recovered, patient's diffusion metrics and FC showed no differences compared to the controls. This single case and its multimodal approach reinforce the crucial role of the right SLF III and DAN in the development and recovery of egocentric and allocentric extra‐personal USN, highlighting the need to preserve these structural and functional areas during brain surgery.
Predictive and reactive behaviors represent two mutually exclusive strategies for successfully completing a sensorimotor task. It is thought that predictive actions are based on the medial premotor system, in the superior frontal gyrus (SFG) and reactive stimulus-response behaviors rely on a lateral premotor system, in the inferior frontal gyrus (IFG). The frontal aslant tract (FAT), a white matter tract connecting SFG and IFG, is a possible neural substrate of the predictive/reactive interactions. We used diffusion-weighted imaging (DWI) of 17 male and female healthy human volunteers, to dissect 3 sub-bundles of fibers belonging to the left FAT (bundles 1, 2 and 3), arising ventrally from 1) the ventral precentral gyrus, 2) midway between the PCG and pars opercularis (POp) and 3) the POp and terminating dorsally in 3 different parts of the SFG, in a caudal-rostral order. We applied online transcranial magnetic stimulation (TMS) to 6 spots, corresponding to the medial and lateral terminations of bundles 1-3 during the fixed-duration set period of a delayed reaction task, that can be solved using a predictive (anticipatory) strategy or with a reactive strategy. Results showed that TMS changed the frequency of predictive/reactive strategies only when applied over 2 spots, the SFG and IFG terminations of bundle 2. Importantly, the effects of TMS were opposite when applied to the SFG or to the IFG. Our data show that the SFG and the IFG have opposite roles in producing predictive or reactive behavior and that reciprocal integration or competition is probably mediated by the FAT.Significance StatementAs is well-known by athletes at starting blocks, interaction with the world can occur with a predictive strategy (anticipating a GO-signal) or a reactive strategy (waiting for the GO-signal to be manifest) and they are mutually exclusive. Here we showed, by using non-invasive brain stimulation (TMS), that two specific cortical regions in the superior frontal gyrus (SFG) and the inferior frontal gyrus (IFG) have opposite roles in facilitating a predictive or a reactive strategy. Importantly these two very distant regions but with highly interconnected functions are specifically connected by a small white matter bundle, which probably mediates the competition between predictive and reactive strategies. More generally, we show that the implementing anatomical connectivity in TMS studies strongly reduces spatial noise.
Tractography is a powerful method to represent the structural connectivity of the brain white matter. Nevertheless, the comparison of these data structures between two individuals is still an open challenge because of their complexity, e.g. digital representation of millions of fibers as polylines. The scientific community spent a meaningful effort to develop new methods of white matter registration aiming to take advantage of diffusion MRI models. Despite the effort to improve the registration of the white matter, little is known about the effect of the registration on tractogram alignment. The main issue for an empirical evaluation is the lack of ground truth, e.g. a sample of data where the correct alignment is validated by experts. This work aims to overcome this drawback by proposing an evaluation framework based on the matching of homologous fiber structures, e.g. known neuroanatomical bundles. The contribution is a quantitative comparison of how the most representative methods of white matter registration affect tractogram alignment.
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in the morphology of the tissues can be incompatible with the prior knowledge embedded within the algorithms. Second, the availability of MR images of distorted brains is very scarce, so the methods in the literature have not addressed such cases so far. In this work, we present the first evaluation of state-of-theart automatic tissue segmentation pipelines on T1-weighted images of brains with different severity of congenital or acquired brain distortion. We compare traditional pipelines and a deep learning model, i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional pipelines completely fail to segment the tissues with strong anatomical distortion. Surprisingly, the 3D U-Net provides useful segmentations that can be a valuable starting point for manual refinement by experts/neuroradiologists.
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