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The association of posterior thalamic strokes with the presence of chronic "thalamic" pain was described in the early 1900s and revisited in a recent review of these patients. Acute pain in corporal structures is associated with the spinothalamic tract (STT), which originates in the dorsal horn of the spinal cord, whereas that associated with cranial structures is associated with the spinal division of the trigeminal nucleus. These pathways terminate in the ventral posterior nucleus (VP), including its posterior and inferior subnuclei and its core, which is classically associated with tactile and haptic functions. In medial nuclei (medial dorsal and intralaminar) receptive fields are large and stimulation evokes diffuse unpleasant sensations and pain while neurons in these nuclei subserve cognitive processes of attention, alerting, and conditioning. In the lateral nuclei neurons have small receptive and projected fields and high resolution of responses to somatic stimuli. Neurons in the lateral nuclei respond to stimuli producing pain, temperature, and visceral sensations while stimulation evokes similar sensations. Small strokes in VP core versus structures located inferior and posterior are associated with thalamic pain and decreased tactile, painful, and cold sensations and with decreased evoked potentials for painful (laser) heat and median nerve stimulation (electrical). Lesions of VP, but not ventral medial posterior nucleus (VMpo), are associated with thalamic pain, contrary to the recent "disinhibition" model. We review the evidence that the lateral nuclei are associated with multiple processes including tactile, nociceptive, visceral, and thermal content of stimuli, whereas the medial nuclei are related to cognitions about those stimuli. [ABSTRACT FROM AUTHOR]

This study introduces OpenMAP‐T1, a deep‐learning‐based method for rapid and accurate whole‐brain parcellation in T1‐ weighted brain MRI, which aims to overcome the limitations of conventional normalization‐to‐atlas‐based approaches and multi‐atlas label‐fusion (MALF) techniques. Brain image parcellation is a fundamental process in neuroscientific and clinical research, enabling a detailed analysis of specific cerebral regions. Normalization‐to‐atlas‐based methods have been employed for this task, but they face limitations due to variations in brain morphology, especially in pathological conditions. The MALF techniques improved the accuracy of the image parcellation and robustness to variations in brain morphology, but at the cost of high computational demand that requires a lengthy processing time. OpenMAP‐T1 integrates several convolutional neural network models across six phases: preprocessing; cropping; skull‐stripping; parcellation; hemisphere segmentation; and final merging. This process involves standardizing MRI images, isolating the brain tissue, and parcellating it into 280 anatomical structures that cover the whole brain, including detailed gray and white matter structures, while simplifying the parcellation processes and incorporating robust training to handle various scan types and conditions. The OpenMAP‐T1 was validated on the Johns Hopkins University atlas library and eight available open resources, including real‐world clinical images, and the demonstration of robustness across different datasets with variations in scanner types, magnetic field strengths, and image processing techniques, such as defacing. Compared with existing methods, OpenMAP‐T1 significantly reduced the processing time per image from several hours to less than 90 s without compromising accuracy. It was particularly effective in handling images with intensity inhomogeneity and varying head positions, conditions commonly seen in clinical settings. The adaptability of OpenMAP‐T1 to a wide range of MRI datasets and its robustness to various scan conditions highlight its potential as a versatile tool in neuroimaging. [ABSTRACT FROM AUTHOR]

Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurological sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional magnetic resonance imaging (MRI). DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measurements in the corpus callosum, thalamus, basal ganglia, corticospinal tract, and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication. [ABSTRACT FROM AUTHOR]

Background: Conventional neuroimaging biomarkers for the neurodegeneration of Alzheimer's disease (AD) are not sensitive enough to detect neurodegenerative alterations during the preclinical stage of AD individuals. Objective: We examined whether neurodegeneration of the entorhinal-hippocampal pathway could be detected along the AD continuum using ultra-high-field diffusion tensor imaging and tractography for ex vivo brain tissues. Methods: Postmortem brain specimens from a cognitively unimpaired individual without AD pathological changes (non-AD), a cognitively unimpaired individual with AD pathological changes (preclinical AD), and a demented individual with AD pathological changes (AD dementia) were scanned with an 11.7T diffusion magnetic resonance imaging. Fractional anisotropy (FA) values of the entorhinal layer II and number of perforant path fibers counted by tractography were compared among the AD continuum. Following the imaging analyses, the status of myelinated fibers and neuronal cells were verified by subsequent serial histological examinations. Results: At 250μm (zipped to 125μm) isotropic resolution, the entorhinal layer II islands and the perforant path fibers could be identified in non-AD and preclinical AD, but not in AD dementia, followed by histological verification. The FA value of the entorhinal layer II was the highest among the entorhinal laminae in non-AD and preclinical AD, whereas the FA values in the entorhinal laminae were homogeneously low in AD dementia. The FA values and number of perforant path fibers decreased along the AD continuum (non-AD>preclinical AD > AD dementia). Conclusion: We successfully detected neurodegenerative alterations of the entorhinal-hippocampal pathway at the preclinical stage of the AD continuum. [ABSTRACT FROM AUTHOR]

The article explores behaviors associated with responses to threatening stimuli, such as pain and visuosensory threats. Neuronal activity in the posterior parietal cortex reflects proximity to threats, and responses to multiple sensory modalities are consistent with this region. Attention to stimuli and conditioning play roles in modulating responses, with implications for chronic pain and coping strategies. The study suggests testable hypotheses related to changes in physiological and cognitive responses to stimuli. [Extracted from the article]

We aimed to compare brain white matter integrity in participants with post-COVID-19 conditions (PCC) and healthy controls. We compared cognitive performance (NIH Toolbox®), psychiatric symptoms and diffusion tensor imaging (DTI) metrics between 23 PCC participants and 24 controls. Fractional anisotropy (FA), axial (AD), radial (RD), and mean (MD) diffusivities were measured in 9 white matter tracts and 6 subcortical regions using MRICloud. Compared to controls, PCC had similar cognitive performance, but greater psychiatric symptoms and perceived stress, as well as higher FA and lower diffusivities in multiple white matter tracts (ANCOVA-p-values≤0.001–0.048). Amongst women, PCC had higher left amygdala-MD than controls (sex-by-PCC p=0.006). Regardless of COVID-19 history, higher sagittal strata-FA predicted greater fatigue (r=0.48-0.52, p<0.001) in all participants, and higher left amygdala-MD predicted greater fatigue (r=0.61, p<0.001) and anxiety (r=0.69, p<0.001) in women, and higher perceived stress (r=0.45, p=0.002) for all participants. Microstructural abnormalities are evident in PCC participants averaged six months after COVID-19. The restricted diffusivity (with reduced MD) and higher FA suggest enhanced myelination or increased magnetic susceptibility from iron deposition, as seen in stress conditions. The higher amygdala-MD in female PCC suggests persistent neuroinflammation, which might contribute to their fatigue, anxiety, and perceived stress. [ABSTRACT FROM AUTHOR]