Recently there has been renewed interest in the habenula; a pair of small, highly evolutionarily conserved epithalamic nuclei adjacent to the medial dorsal (MD) nucleus of the thalamus. The habenula has been implicated in a range of behaviours including sleep, stress and pain, and studies in non-human primates have suggested a potentially important role in reinforcement processing, putatively via its effects on monoaminergic neurotransmission. Over the last decade, an increasing number of neuroimaging studies have reported functional responses in the human habenula using functional magnetic resonance imaging (fMRI). However, standard fMRI analysis approaches face several challenges in isolating signal from this structure because of its relatively small size, around 30 mm(3) in volume. In this paper we offer a set of guidelines for locating and manually tracing the habenula in humans using high-resolution T1-weighted structural images. We also offer recommendations for appropriate pre-processing and analysis of high-resolution functional magnetic resonance imaging (fMRI) data such that signal from the habenula can be accurately resolved from that in surrounding structures.
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http://dx.doi.org/10.1016/j.neuroimage.2012.08.076 | DOI Listing |
Eur J Trauma Emerg Surg
January 2025
Department of Neurology, Haaglanden Medical Center, PO Box 432, 2501 CK, The Hague, The Netherlands.
Background And Importance: Traumatic intracranial hemorrhage (tICH) after mild traumatic brain injury (mTBI) is not uncommon in the elderly. Often, these patients are admitted to the hospital for observation. The necessity of admission in the absence of clinically important intracranial injuries is however unclear.
View Article and Find Full Text PDFBMC Neurol
January 2025
Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
Background: Cerebral venous sinus thrombosis (CVST) is a rare yet significant neurological disorder with high mortality. Understanding its evolving characteristics, risk factors, and outcomes, particularly in Chinese patients after the COVID-19 pandemic, is critical for developing effective preventive and therapeutic strategies.
Methods: A retrospective analysis was conducted on 471 CVST cases from Xuanwu Hospital, comparing data before (2013-2017, n = 243) and after (2021-2023, n = 228) the COVID-19 pandemic.
Commun Biol
January 2025
School of Psychology, Shenzhen University, Shenzhen, China.
Speech processing involves a complex interplay between sensory and motor systems in the brain, essential for early language development. Recent studies have extended this sensory-motor interaction to visual word processing, emphasizing the connection between reading and handwriting during literacy acquisition. Here we show how language-motor areas encode motoric and sensory features of language stimuli during auditory and visual perception, using functional magnetic resonance imaging (fMRI) combined with representational similarity analysis.
View Article and Find Full Text PDFTransl Psychiatry
January 2025
Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore.
Depression treatment responses vary widely among individuals. Identifying objective biomarkers with predictive accuracy for therapeutic outcomes can enhance treatment efficiency and avoid ineffective therapies. This study investigates whether functional near-infrared spectroscopy (fNIRS) and clinical assessment information can predict treatment response in major depressive disorder (MDD) through machine-learning techniques.
View Article and Find Full Text PDFBiometrics
January 2025
Department of Biostatistics, Brown University, Providence, RI 02912, United States.
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework's backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain.
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