BACKGROUND: Radical radiotherapy (RT) is the cornerstone of Head and Neck (H&N) cancer treatment, but it often leads to fatigue due to irradiation of brain structures, impacting patient quality of life.
OBJECTIVE: This study aimed to systematically investigate the dose correlates of fatigue after H&N RT in brain structures.
METHODS: The systematic review included studies that examined the correlation between fatigue outcomes in H&N cancer patients undergoing RT at different time intervals and brain structures. PubMed, Scopus, and WOS databases were used in the systematic review. A methodological quality assessment of the included studies was conducted following the PRISMA guidelines. After RT, the cohort of H&N cancer patients was analyzed for dose correlations with brain structures and substructures, such as the posterior fossa, brainstem, cerebellum, pituitary gland, medulla, and basal ganglia.
RESULTS: Thirteen studies meeting the inclusion criteria were identified in the search. These studies evaluated the correlation between fatigue and RT dose following H&N RT. The RT dose ranged from 40 Gy to 70 Gy. Most of the studies indicated a correlation between the trajectory of fatigue and the dose effect, with higher levels of fatigue associated with increasing doses. Furthermore, five studies found that acute and late fatigue was associated with dose volume in specific brain structures, such as the brain stem, posterior fossa, cerebellum, pituitary gland, hippocampus, and basal ganglia.
CONCLUSIONS: Fatigue in H&N RT patients is related to the radiation dose received in specific brain areas, particularly in the posterior fossa, brain stem, cerebellum, pituitary gland, medulla, and basal ganglia. Dose reduction in these areas may help alleviate fatigue. Monitoring fatigue in high-risk patients after radiation therapy could be beneficial, especially for those experiencing late fatigue.

The cerebellum has a large network of neurons that communicate with several brain structures and participate in different functions. Recent studies have demonstrated that the cerebellum is not only associated with motor functions but also participates in several non-motor functions. It is suggested that the cerebellum can modulate behavior through many connections with different nervous system structures in motor, sensory, cognitive, autonomic, and emotional processes. Recently, a growing number of clinical and experimental studies support this theory and provide further evidence. In light of recent findings, a comprehensive review is needed to summarize the knowledge on the influence of the cerebellum on the processing of different functions. Therefore, the aim of this review was to describe the neuroanatomical aspects of the activation of the cerebellum and its connections with other structures of the central nervous system in different behaviors.

Direct and indirect links between brain regions and cardiac function have been reported. We performed a systematic literature review to summarize current knowledge regarding the associations of heart rate variability (HRV) and brain region morphology, activity and connectivity involved in autonomic control at rest in healthy subjects. Both positive and negative correlations of cortical thickness and gray matter volumes of brain structures with HRV were observed. The strongest were found for a cluster located within the cingulate cortex. A decline in HRV, as well as cortical thickness with increasing age, especially in the orbitofrontal cortex were noted. When associations of region-specific brain activity with HRV were examined, HRV correlated most strongly with activity in the insula, cingulate cortex, frontal and prefrontal cortices, hippocampus, thalamus, striatum and amygdala. Furthermore, significant correlations, largely positive, between HRV and brain region connectivity (in the amygdala, cingulate cortex and prefrontal cortex) were observed. Notably, right-sided neural structures may be preferentially involved in heart rate and HRV control. However, the evidence for left hemispheric control of cardiac vagal function has also been reported. Our findings provide support for the premise that the brain and the heart are interconnected by both structural and functional networks and indicate complex multi-level interactions. Further studies of brain-heart associations promise to yield insights into their relationship to health and disease.

New brain technologies including neuroimaging studies are powerful means for providing new insights into clinical and cognitive neuroscience. Bipolar disorder is a severe chronic phasic mental disease characterized by various cognitive dysfunctions. Working memory is one prominent domain of cognitive impairment in bipolar disorder. Disruptions in working memory are observed even in euthymic bipolar patients which makes it a potential endophenotypic marker for the disorder. Finding such markers may help in providing firm neurobiological basis for psychiatric nosologies and symptomatic presentations. This review aims to summarize some of the important aspects of findings from functional magnetic resonance imaging studies on the activation of brain structures in relation to working memory paradigms.

Automatic brain structures segmentation in magnetic resonance images has been widely investigated in recent years with the goal of helping diagnosis and patient follow-up in different brain diseases. Here, we present a review of the state-of-the-art of automatic methods available in the literature ranging from structure specific segmentation methods to whole brain parcellation approaches.
We divide first the algorithms according to their target structures and then we propose a general classification based on their segmentation strategy, which includes atlas-based, learning-based, deformable, region-based and hybrid methods. We further discuss each category\'s strengths and weaknesses and analyze its performance in segmenting different brain structures providing a qualitative and quantitative comparison.
We compare the results of the analyzed works for the following brain structures: hippocampus, thalamus, caudate nucleus, putamen, pallidum, amygdala, accumbens, lateral ventricles, and brainstem. The structures on which more works have focused on are the hippocampus and the caudate nucleus. In general, the accumbens (0.69 mean DSC) is the most difficult structure to segment whereas the structures that seem to get the best results are the brainstem, closely followed by the thalamus and the putamen with 0.88, 0.87 and 0.86 mean DSC, respectively. Atlas-based approaches achieve good results when segmenting the hippocampus (DSC between 0.75 and 0.90), thalamus (0.88-0.92) and lateral ventricles (0.83-0.93), while deformable methods perform good for caudate nucleus (0.84-0.91) and putamen segmentation (0.86-0.89).
There is not yet a single automatic segmentation approach that can emerge as a standard for the clinical practice, providing accurate brain structures segmentation. Future trends need to focus on combining multi-atlas methods with learning-based or deformable approaches. Employing atlases to provide spatial robustness and modeling the structures appearance with supervised classifiers or Active Appearance Models could lead to improved segmentation results.