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In the T2DM group, the FC between the left cerebellar lobule VI and the right precuneus was negatively correlated with the Trail Making Test A (TMT-A) score (r = -0.430, P = 0.013), after a Bonferroni correction. In conclusion, patients with T2DM have altered FC between the cerebellar subregions and the cerebral networks involved in cognitive and emotional processing. This suggests that a range of cerebellar-cerebral circuits may be involved in the neuropathology of T2DM cognitive dysfunction.Neuroimaging evidence has suggested white matter microstructure are heavily affected in Alzheimer's disease (AD). However, whether white matter dysfunction is localized at the specific regions of fiber tracts and whether they would be a potential biomarker for AD remain unclear. By automated fiber quantification (AFQ), we applied diffusion tensor images from 25 healthy controls (HC), 24 amnestic mild cognitive impairment (aMCI) patients and 18 AD patients to create tract profiles along 16 major white matter fibers. We compared diffusion metrics [Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (DA), and radial diffusivity (DR)] between groups. To assess the diagnostic value, we applied a random forest (RF) classifier, a type of machine learning method. In the global tract level, we found that aMCI and AD patients showed higher MD, DA, and DR values in some fiber tracts mostly in the left hemisphere compared to HC. In the point-wise level, widespread disruption were distributed on specific locations of different tracts. The point-wise MD measurements presented the best classification performance with respect to differentiating AD from HC. The two most important variables were localized in the prefrontal potion of left uncinate fasciculus and anterior thalamic radiation. In addition, the point-wise DA in the posterior component of the left cingulum cingulate displayed the most robust discriminative ability to identify AD from aMCI. Our findings provide evidence that white matter abnormalities based on the AFQ method could be as a diagnostic biomarker in AD.In independent component analysis (ICA), the selection of model order (i.e., number of components to be extracted) has crucial effects on functional magnetic resonance imaging (fMRI) brain network analysis. Model order selection (MOS) algorithms have been used to determine the number of estimated components. However, simulations show that even when the model order equals the number of simulated signal sources, traditional ICA algorithms may misestimate the spatial maps of the signal sources. In principle, increasing model order will consider more potential information in the estimation, and should therefore produce more accurate results. However, this strategy may not work for fMRI because large-scale networks are widely spatially distributed and thus have increased mutual information with noise. As such, conventional ICA algorithms with high model orders may not extract these components at all. This conflict makes the selection of model order a problem. We present a new strategy for model order free ICA, called Snowball ICA, that obviates these issues. The algorithm collects all information for each network from fMRI data without the limitations of network scale. Triton X-114 Using simulations and in vivo resting-state fMRI data, our results show that component estimation using Snowball ICA is more accurate than traditional ICA. The Snowball ICA software is available at https//github.com/GHu-DUT/Snowball-ICA.In contrast to mammals, the adult zebrafish brain shows neurogenic activity in a multitude of niches present in almost all brain subdivisions. Irrespectively, constitutive neurogenesis in the adult zebrafish and mouse telencephalon share many similarities at the cellular and molecular level. However, upon injury during tissue repair, the situation is entirely different. In zebrafish, inflammation caused by traumatic brain injury or by induced neurodegeneration initiates specific and distinct neurogenic programs that, in combination with signaling pathways implicated in constitutive neurogenesis, quickly, and efficiently overcome the loss of neurons. In the mouse brain, injury-induced inflammation promotes gliosis leading to glial scar formation and inhibition of regeneration. A better understanding of the regenerative mechanisms occurring in the zebrafish brain could help to develop new therapies to combat the debilitating consequences of brain injury, stroke, and neurodegeneration. The aim of this review is to compare the properties of neural progenitors and the signaling pathways, which control adult neurogenesis and regeneration in the zebrafish and mammalian telencephalon.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that causes degeneration of the lower and upper motor neurons and is the most prevalent motor neuron disease. This disease is characterized by muscle weakness, stiffness, and hyperreflexia. Patients survive for a short period from the onset of the disease. Most cases are sporadic, with only 10% of the cases being genetic. Many genes are now known to be involved in familial ALS cases, including some of the sporadic cases. It has also been observed that, in addition to genetic factors, there are numerous molecular mechanisms involved in these pathologies, such as excitotoxicity, mitochondrial disorders, alterations in axonal transport, oxidative stress, accumulation of misfolded proteins, and neuroinflammation. This pathology affects the motor neurons, the spinal cord, the cerebellum, and the brain, but recently, it has been shown that it also affects the visual system. This impact occurs not only at the level of the oculomotor system but also at the retinal level, which is why the retina is being proposed as a possible biomarker of this pathology. The current review discusses the main aspects mentioned above related to ALS, such as the main genes involved, the most important molecular mechanisms that affect this pathology, its ocular involvement, and the possible usefulness of the retina as a biomarker.Neonatal intensive care units (NICUs) greatly expand the use of technology. There is a need to accurately diagnose discomfort, pain, and complications, such as sepsis, mainly before they occur. While specific treatments are possible, they are often time-consuming, invasive, or painful, with detrimental effects for the development of the infant. In the last 40 years, heart rate variability (HRV) has emerged as a non-invasive measurement to monitor newborns and infants, but it still is underused. Hence, the present paper aims to review the utility of HRV in neonatology and the instruments available to assess it, showing how HRV could be an innovative tool in the years to come. When continuously monitored, HRV could help assess the baby's overall wellbeing and neurological development to detect stress-/pain-related behaviors or pathological conditions, such as respiratory distress syndrome and hyperbilirubinemia, to address when to perform procedures to reduce the baby's stress/pain and interventions, such as therapeutic hypothermia, and to avoid severe complications, such as sepsis and necrotizing enterocolitis, thus reducing mortality.