BACKGROUND: In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment.
METHODS: A total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks\' taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients.
RESULTS: This study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856.
CONCLUSIONS: The study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

We assessed a genetic risk score for Alzheimer\'s disease (AD-GRS) and apolipoprotein E (APOE4) in an exploratory neuroimaging substudy of the FINGER trial.
1260 at-risk older individuals without dementia were randomized to multidomain lifestyle intervention or health advice. N = 126 participants underwent magnetic resonance imaging (MRI), and N = 47 positron emission tomography (PET) scans (Pittsburgh Compund B [PiB], Fluorodeoxyglucose) at baseline; N = 107 and N = 38 had repeated 2-year scans.
The APOE4 allele, but not AD-GRS, was associated with baseline lower hippocampus volume (β = -0.27, p = 0.001), greater amyloid deposition (β = 0.48, p = 0.001), 2-year decline in hippocampus (β = -0.27, p = 0.01), total gray matter volume (β = -0.25, p = 0.01), and cortical thickness (β = -0.28, p = 0.003). In analyses stratified by AD-GRS (below vs above median), the PiB composite score increased less in intervention versus control in the higher AD-GRS group (β = -0.60, p = 0.03).
AD-GRS and APOE4 may have different impacts on potential intervention effects on amyloid, that is, less accumulation in the higher-risk group (AD-GRS) versus lower-risk group (APOE).
First study of neuroimaging and AD genetics in a multidomain lifestyle intervention. Possible intervention effect on brain amyloid deposition may rely on genetic risk. AD-GRS and APOE4 allele may have different impacts on amyloid during intervention.

UNASSIGNED: Parkinson\'s disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health.
UNASSIGNED: This study selected 227 cases from the Parkinson\'s Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case\'s fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model.
UNASSIGNED: In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively.
UNASSIGNED: The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.

The existence of lymphatic vessels or similar clearance systems in the central nervous system (CNS) that transport nutrients and remove cellular waste is a neuroscientific question of great significance. As the brain is the most metabolically active organ in the body, there is likely to be a potential correlation between its clearance system and the pathological state of the CNS. Until recently the successive discoveries of the glymphatic system and the meningeal lymphatics solved this puzzle. This article reviews the basic anatomy and physiology of the glymphatic system. Imaging techniques to visualize the function of the glymphatic system mainly including post-contrast imaging techniques, indirect lymphatic assessment by detecting increased perivascular space, and diffusion tensor image analysis along the perivascular space (DTI-ALPS) are discussed. The pathological link between glymphatic system dysfunction and neurological disorders is the key point, focusing on the enlarged perivascular space (EPVS) and the index of diffusivity along the perivascular space (ALPS index), which may represent the activity of the glymphatic system as possible clinical neuroimaging biomarkers of neurological disorders. The pathological link between glymphatic system dysfunction and neurological disorders is the key point, focusing on the enlarged perivascular space (EPVS) and the index for of diffusivity along the perivascular space (ALPS index), which may represent the activity of the glymphatic system as possible clinical neuroimaging biomarkers of neurological disorders.

UNASSIGNED: Alzheimer\'s disease (AD) is a prevalent neurodegenerative disorder that poses a worldwide public health challenge. A neuroimaging biomarker would significantly improve early diagnosis and intervention, ultimately enhancing the quality of life for affected individuals and reducing the burden on healthcare systems.
UNASSIGNED: Cross-sectional and longitudinal data (10,099 participants with 13,380 scans) from 12 independent datasets were used in the present study (this study was performed between September 1, 2021 and February 15, 2023). The Individual Brain-Related Abnormalities In Neurodegeneration (IBRAIN) score was developed via integrated regional- and network-based measures under an ensemble machine learning model based on structural MRI data. We systematically assessed whether IBRAIN could be a neuroimaging biomarker for AD.
UNASSIGNED: IBRAIN accurately differentiated individuals with AD from NCs (AUC = 0.92) and other neurodegenerative diseases, including Frontotemporal dementia (FTD), Parkinson\'s disease (PD), Vascular dementia (VaD) and Amyotrophic Lateral Sclerosis (ALS) (AUC = 0.92). IBRAIN was significantly correlated to clinical measures and gene expression, enriched in immune process and protein metabolism. The IBRAIN score exhibited a significant ability to reveal the distinct progression of prodromal AD (i.e., Mild cognitive impairment, MCI) (Hazard Ratio (HR) = 6.52 [95% CI: 4.42∼9.62], p < 1 × 10-16), which offers similar powerful performance with Cerebrospinal Fluid (CSF) Aβ (HR = 3.78 [95% CI: 2.63∼5.43], p = 2.13 × 10-14) and CSF Tau (HR = 3.77 [95% CI: 2.64∼5.39], p = 9.53 × 10-15) based on the COX and Log-rank test. Notably, the IBRAIN shows comparable sensitivity (beta = -0.70, p < 1 × 10-16) in capturing longitudinal changes in individuals with conversion to AD than CSF Aβ (beta = -0.26, p = 4.40 × 10-9) and CSF Tau (beta = 0.12, p = 1.02 × 10-5).
UNASSIGNED: Our findings suggested that IBRAIN is a biologically relevant, specific, and sensitive neuroimaging biomarker that can serve as a clinical measure to uncover prodromal AD progression. It has strong potential for application in future clinical practice and treatment trials.
UNASSIGNED: Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, Beijing Natural Science Funds, the Fundamental Research Funds for the CentralUniversity, and the Startup Funds for Talents at Beijing Normal University.

Clear prognostic indicators of cognitive behavioural therapy (CBT) are lacking for depression. This study aims to identify a biomarker that predicts CBT outcomes in depression. We developed a machine learning algorithm to predict post-CBT Hamilton Depression Rating Scale (HAMD) using pre-CBT regional homogeneity (ReHo). We examined transcriptomic signatures of regions with CBT-related ReHo changes. Twenty-five patients completed CBT and had increased ReHo in the dorsolateral prefrontal cortex (DLPFC) following CBT. Pre-CBT ReHo in left DLPFC was shown to be a predictor of post-HAMD scores. We identified left DLPFC ReHo as a neuroimaging biomarker for therapeutic effects of CBT in depression.

UNASSIGNED: Magnetic susceptibility changes in brain MRI of Wilson\'s disease (WD) patients have been described in subcortical nuclei especially the basal ganglia. The objectives of this study were to investigate its relationship with other microstructural and functional alterations of the subcortical nuclei and the diagnostic utility of these MRI-related metrics.
UNASSIGNED: A total of 22 WD patients and 20 healthy controls (HCs) underwent 3.0T multimodal MRI scanning. Susceptibility, volume, diffusion microstructural indices and whole-brain functional connectivity of the putamen (PU), globus pallidus (GP), caudate nucleus (CN), and thalamus (TH) were analyzed. Receiver operating curve (ROC) was applied to evaluate the diagnostic value of the imaging data. Correlation analysis was performed to explore the connection between susceptibility change and microstructure and functional impairment of WD and screen for neuroimaging biomarkers of disease severity.
UNASSIGNED: Wilson\'s disease patients demonstrated increased susceptibility in the PU, GP, and TH, and widespread atrophy and microstructural impairments in the PU, GP, CN, and TH. Functional connectivity decreased within the basal ganglia and increased between the PU and cortex. The ROC model showed higher diagnostic value of isotropic volume fraction (ISOVF, in the neurite orientation dispersion and density imaging model) compared with susceptibility. Severity of neurological symptoms was correlated with volume and ISOVF. Susceptibility was positively correlated with ISOVF in GP.
UNASSIGNED: Microstructural impairment of the basal ganglia is related to excessive metal accumulation in WD. Brain atrophy and microstructural impairments are useful neuroimaging biomarkers for the neurological impairment of WD.

In this study we evaluate the performance of a fully automated analytical framework for FDOPA PET neuroimaging data, and its sensitivity to demographic and experimental variables and processing parameters. An instance of XNAT imaging platform was used to store the King\'s College London institutional brain FDOPA PET imaging archive, alongside individual demographics and clinical information. By re-engineering the historical Matlab-based scripts for FDOPA PET analysis, a fully automated analysis pipeline for imaging processing and data quantification was implemented in Python and integrated in XNAT. The final data repository includes 892 FDOPA PET scans organized from 23 different studies. We found good reproducibility of the data analysis by the automated pipeline (in the striatum for the Kicer: for the controls ICC = 0.71, for the psychotic patients ICC = 0.88). From the demographic and experimental variables assessed, gender was found to most influence striatal dopamine synthesis capacity (F = 10.7, p < 0.001), with women showing greater dopamine synthesis capacity than men. Our automated analysis pipeline represents a valid resourse for standardised and robust quantification of dopamine synthesis capacity using FDOPA PET data. Combining information from different neuroimaging studies has allowed us to test it comprehensively and to validate its replicability and reproducibility performances on a large sample size.

Objective:Subjective tinnitus is an auditory phantom perceptual disorder without an objective biomarker. Fast and efficient diagnostic tools will advance clinical practice by detecting or confirming the condition, tracking change in severity, and monitoring treatment response. Motivated by evidence of subtle anatomical, morphological, or functional information in magnetic resonance images of the brain, we examine data-driven machine learning methods for joint tinnitus classification (tinnitus or no tinnitus) and tinnitus severity prediction.Approach:We propose a deep multi-task multimodal framework for tinnitus classification and severity prediction using structural MRI (sMRI) data. To leverage complementary information multimodal neuroimaging data, we integrate two modalities of three-dimensional sMRI-T1 weighted (T1w) and T2 weighted (T2w) images. To explore the key components in the MR images that drove task performance, we segment both T1w and T2w images into three different components-cerebrospinal fluid, grey matter and white matter, and evaluate performance of each segmented image.Main results:Results demonstrate that our multimodal framework capitalizes on the information across both modalities (T1w and T2w) for the joint task of tinnitus classification and severity prediction.Significance:Our model outperforms existing learning-based and conventional methods in terms of accuracy, sensitivity, specificity, and negative predictive value.

Bipolar disorder (BD) is a psychiatric disorder characterized by episodes of depression and mania, associated with impaired emotion processing. Several functional MRI (fMRI) studies have been used to investigate the structural and functional alteration in BD. Here, we aim to investigate the current fMRI findings of brain activation during emotion-regulation tasks between BD patients and healthy controls (HC).
A systematic search through PubMed database for fMRI studies on bipolar patients and HC yielded 685 studies. We performed an activation likelihood estimation (ALE) on 21 studies for emotion regulation in BD patients and HC. Furthermore, we performed subgroup analyses for task performances in response time and accuracy between bipolar patients and HC.
The total sample included 21 fMRI studies, comprising 543 BD patients, compared to 565 HC. ALE maps for emotion-related tasks showed hyperactivation in BD patients in the caudate, amygdala, precentral gyrus, middle frontal gyri, and sub-gyrus. Whereas hypoactivation was seen in the inferior frontal gyrus and anterior cingulate gyrus.
We could not apply a correction for p-value thresholds, as it needs large number of foci. Second, functional abnormalities were investigated for adult BD patients only, as BD patients have functional differences correlated with age.
Our results showed that limbic and cortical regions can represent a potential biomarker for the diagnosis and management of BD, by showing clustered brain regions of abnormal patterns of increased activation between BD patients and HC.