Objective This study assessed the associations of the abnormal brain activation and functional connectivity (FC) during memory processing and brain volume alteration in conjunction with psychiatric symptom severity in patients with obsessive-compulsive disorder (OCD).Methods Twenty-OCD patients and 20-healthy controls (HC) underwent T1-weighted and functional imaging underlying explicit memory task.Results In memory encoding, OCD patients showed higher activities in right/left (Rt./Lt.) inferior temporal gyrus (ITG), medial prefrontal cortex (MPFC), dorsolateral prefrontal cortex (DLPFC) and anterior cingulate cortex (ACC), compared with HC. In task-based FC, caudate (Cd) was positively connected with DLPFC and ITG in OCD, while HC showed different connectivities of Cd-ACC and Rt.-Lt. ITG. In memory retrieval, only Cd was activated in OCD patients. Cd was positively connected with DLPFC and vmPFC in OCD, but negatively connected between same brain areas in HC. OCD patients showed increased gray matter (GM) volumes of cerebellum, DLPFC, orbitofrontal cortex (OFC), hippocampus, Cd and ITG, and concurrently, increased white matter volumes of DLPFC. In OCD patients, GM volumes of Cd and OFC were positively correlated with HAMA and Y-BOCS. Functional activity changes of Cd in OCD were positively correlated with Y-BOCS.Conclusion Our findings support to accessing clinical symptom and its severity linked by brain structural deformation and functional abnormality in OCD patients.

Even though many previous studies have reported structural or functional brain abnormalities in patients with alcohol dependence (ADPs), studies observing the structural and functional abnormalities associated with the clinical characteristics of ADPs utilizing a multimodal approach are still scarce. The aim of this study was to demonstrate structural and functional brain abnormalities and their association with the clinical characteristics of alcoholism in male ADPs. Fifteen healthy male controls (HCs) and 15 male ADPs who had been diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders 5 criteria underwent T1-weighted imaging and resting-state functional magnetic resonance imaging (MRI) scans. The MRI data were postprocessed using statistical parametric mapping for structural analysis and CONN-fMRI functional connectivity (FC) tools for functional analysis. In comparison with male HCs, male ADPs were characterized by significantly reduced volumes of the white matter in the left globus pallidus (GP) (p-FDR < 0.05). This region affected the altered resting-state FC patterns in male ADPs. Interestingly, an abnormal FC in the precuneus and its positive correlation with the alcohol-use disorder identification test score were observed in ADPs (r = 0.546, p = 0.036). Based on the observations, it could be concluded that the GP serves as a neural marker that impacts abnormal functional networks in men with alcohol dependence. These findings have important clinical implications as they provide insights into the neural mechanism underlying the anatomical, functional, and clinical features of alcoholism.

Shin-Eui Park, Yun-Hyeon Kim, Jong-Chul Yang, Gwang-Woo Jeong. Clin Psychopharmacol Neurosci 2022;20:279-91. https://doi.org/10.9758/cpn.2022.20.2.279

The morphology of the brain undergoes changes throughout the aging process, and accurately predicting a person’s brain age and gender using brain morphology features can aid in detecting atypical brain patterns. Neuroimaging-based estimation of brain age is commonly used to assess an individual’s brain health relative to a typical aging trajectory, while accurately classifying gender from neuroimaging data offers valuable insights into the inherent neurological differences between males and females. In this study, we aimed to compare the efficacy of classical machine learning models with that of a quantum machine learning method called a variational quantum circuit in estimating brain age and predicting gender based on structural magnetic resonance imaging data. We evaluated six classical machine learning models alongside a quantum machine learning model using both combined and sub-datasets, which included data from both in-house collections and public sources. The total number of participants was 1157, ranging from ages 14 to 89, with a gender distribution of 607 males and 550 females. Performance evaluation was conducted within each dataset using training and testing sets. The variational quantum circuit model generally demonstrated superior performance in estimating brain age and gender classification compared to classical machine learning algorithms when using the combined dataset. Additionally, in benchmark sub-datasets, our approach exhibited better performance compared to previous studies that utilized the same dataset for brain age prediction. Thus, our results suggest that variational quantum algorithms demonstrate comparable effectiveness to classical machine learning algorithms for both brain age and gender prediction, potentially offering reduced error and improved accuracy.

Magnetic resonance spectroscopy (MRS) is a noninvasive technique for measuring metabolite concentration. It can be used for preclinical small animal brain studies using rodents to provide information about neurodegenerative diseases and metabolic disorders. However, data acquisition from small volumes in a limited scan time is technically challenging due to its inherently low sensitivity. To mitigate this problem, this study investigated the feasibility of a low-rank denoising method in enhancing the quality of single voxel multinuclei (³¹P and ¹H) MRS data at 9.4 T. Performance was evaluated using in vivo MRS data from a normal mouse brain (³¹P and ¹H) and stroke mouse model (¹H) by comparison with signal-to-noise ratios (SNRs), Cramer-Rao lower bounds (CRLBs), and metabolite concentrations of a linear combination of model analysis results. In ³¹P MRS data, low-rank denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared with the original data. In ¹H MRS data, the method also improved the SNRs, CRLBs, but it performed better for ³¹P MRS data with relatively simpler patterns compared to the ¹H MRS data. Therefore, we suggest that the low-rank denoising method can improve spectra SNR and metabolite quantification uncertainty in single-voxel in vivo ³¹P and ¹H MRS data, and it might be more effective for ³¹P MRS data. The main contribution of this study is that we demonstrated the effectiveness of the low-rank denoising method on small-volume single-voxel MRS data. We anticipate that our results will be useful for the precise quantification of low-concentration metabolites, further reducing data acquisition voxel size, and scan time in preclinical MRS studies.

Objective: Although there have been several studies on the neuroanatomical changes in idiopathic central precocious puberty (ICPP), the association between each brain region and ICPP has not yet been clearly elucidated. This study aimed to evaluate the difference in brain structure in ICPP compared with age-matched healthy controls and normal puberty controls, and additionally the correlation between brain volume difference and the luteinizing hormone (LH). Materials and Methods: The study enrolled fifteen girls with ICPP, as well as 15 age-matched healthy girls and 15 normal puberty girls as controls. The subjects underwent a 1.5 Tesla Avanto MR Scanner. Anatomical T1-weighted images were acquired with a T1 spin-echo sequence. The volumes of total and regional brain were compared with each of the two control groups and analyzed through the paired T-test, and the brain region related to the peak LH level was also analyzed through a simple correlation test. Results: The mean age of the ICPP group, age-matched group, and puberty group were 8.0 ± 0.9 years, 7.8 ± 0.9 years, and 11.9 ± 0.9 years, respectively. In our findings, the regional cerebral volumes in ICPP were different from age-matched controls. Compared with controls, ICPP showed a significant increase in gray matter (GM) volumes (the medial prefrontal cortex, superior parietal gyrus, supramarginal gyrus, angular gyrus, postcentral gyrus, superior occipital gyrus, cuneus, hippocampus, parahippocampal gyrus, posterior cingulate gyrus (PCgG), cerebellar cortex (Cb)) and in white matter (WM) volumes (the insular, caudate, splenium of corpus callosum (p < 0.001)). Especially, the GM volumes of the PCgG (r = 0.57, p = 0.03) and Cb (r = 0.53, p = 0.04) were correlated positively with LH concentrations stimulated by the gonadotropin-releasing hormone agonist. Compared to the normal puberty control, no significant difference in GM volume was found. Conclusions: This study showed the overall brain volumetric differences between ICPP girls and age-matched controls using voxel-based morphometric analysis, and further showed the correlation between brain volume and the sex hormone in ICPP. Through a comparison between the two groups, the cerebral development pattern of ICPP is similar to that of normal puberty, and these local differences in cerebral volume may affect social and congenital changes. These findings will be useful for understanding the neuroanatomical mechanisms on the specific morphological variations associated with ICPP.