Background:
Autism Spectrum Disorder (ASD) is a complex developmental disorder in
which neurological basis is largely unknown. The Corpus Callosum (CC) is the main commissure
that connects the cerebral hemispheres. Previous evidence suggests the involvement of the CC in
the pathophysiology of autism.
Aim:
The aim of our study is to assess whether there were any changes in Corpus Callosum (CC)
area and volume and to reveal the relationship between Diffusion Tensor Imaging (DTI) features in
genu and splenium of corpus callosum in children with ASD.
Methods:
Eighteen patient and 15 controls were recruited. The volumetric sagittal TI images were
used to provide measurements of midsagittal corpus callosum surface area while FA, MD, RD, and
ADC values were extracted from genu and splenium of corpus callosum after which the correlation
in the area and volume in ASD children was examined.
Results:
CC area and volume in children with ASD were decreased than controls. FA values obtained
from the genu and splenum of CC were significantly lower and RD values were significantly
higher. A positive correlation was observed between the FA of the genu and splenium and area and
volume of the CC. There was a negative correlation between ADC, MD and RD of CC and area
and volume measurements.
Conclusion:
The conclusions in the interrelations of morphometric and DTI data may demonstrate
a likelihood of damages in the axons and cortical neurons. The results showed that there existed
microstructural damages from the DTI findings. Furthermore, the decrease in FA could be a representation
of the reduction in the myelination in nerve pathways, impaired integrity, reduced axonal
density, and organization. Indeed, the changes in volumetric and microstructural of CC could be
useful in evaluating underlying pathophysiology in children with autism.
ObjectivesArtificial intelligence (AI) applications in neurosurgery have an increasing momentum as well as the growing number of implementations in the medical literature. In recent years, AI research define a link between neuroscience and AI. It is a connection between knowing and understanding the brain and how to simulate the brain. The machine learning algorithms, as a subset of AI, are able to learn with experiences, perform big data analysis, and fulfill human-like tasks. Intracranial surgical approaches that have been defined, disciplined, and developed in the last century have become more effective with technological developments. We aimed to define individual-safe, intracranial approaches by introducing functional anatomical structures and pathological areas to artificial intelligence.MethodsPreoperative MR images of patients with deeply located brain tumors were used for planning. Intracranial arteries, veins, and neural tracts are listed and numbered. Voxel values of these selected regions in cranial MR sequences were extracted and labeled. Tumor tissue was segmented as the target. Q-learning algorithm which is a model-free reinforcement learning algorithm was run on labeled voxel values (on optimal paths extracted from the new heuristic-based path planning algorithm), then the algorithm was assigned to list the cortico-tumoral pathways that aim to remove the maximum tumor tissue and in the meantime that functional anatomical tissues will be least affected.ResultsThe most suitable cranial entry areas were found with the artificial intelligence algorithm. Cortico-tumoral pathways were revealed using Q-learning from these optimal points.ConclusionsAI will make a significant contribution to the positive outcomes as its use in both preoperative surgical planning and intraoperative technique equipment assisted neurosurgery, its use increased.
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