Structural Magnetic Resonance Imaging Scans Research Articles

Decoding cognitive health using machine learning: A comprehensive evaluation for diagnosis of significant memory concern

AbstractThe timely identification of significant memory concern (SMC) is crucial for proactive cognitive health management, especially in an aging population. Detecting SMC early enables timely intervention and personalized care, potentially slowing cognitive disorder progression. This study presents a state‐of‐the‐art review followed by a comprehensive evaluation of machine learning models within the randomized neural networks (RNNs) and hyperplane‐based classifiers (HbCs) family to investigate SMC diagnosis thoroughly. Utilizing the Alzheimer's Disease Neuroimaging Initiative 2 (ADNI2) dataset, 111 individuals with SMC and 111 healthy older adults are analyzed based on T1W magnetic resonance imaging (MRI) scans, extracting rich features. This analysis is based on baseline structural MRI (sMRI) scans, extracting rich features from gray matter (GM), white matter (WM), Jacobian determinant (JD), and cortical thickness (CT) measurements. In RNNs, deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) emerge as the best classifiers in terms of performance metrics in the identification of SMC. In HbCs, Kernelized pinball general twin support vector machine (Pin‐GTSVM‐K) excels in CT and WM features, whereas Linear Pin‐GTSVM (Pin‐GTSVM‐L) and Linear intuitionistic fuzzy TSVM (IFTSVM‐L) performs well in the JD and GM features sets, respectively. This comprehensive evaluation emphasizes the critical role of feature selection, feature based‐interpretability and model choice in attaining an effective classifier for SMC diagnosis. The inclusion of statistical analyses further reinforces the credibility of the results, affirming the rigor of this analysis. The performance measures exhibit the suitability of this framework in aiding researchers with the automated and accurate assessment of SMC. The source codes of the algorithms and datasets used in this study are available at https://github.com/mtanveer1/SMC.This article is categorized under: Technologies > Classification Technologies > Machine Learning Application Areas > Health Care

Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study

BackgroundLimited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet.MethodsParticipants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman’s rank-order correlation coefficient.ResultsThirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$($$\\end{document}± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant.ConclusionsAcquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample.Trial registrationThe study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538).

Identification of Severe Grading in Knee OsteoArthritis from MRI using Ensemble Deep Learning.

Accurate finding of Knee Osteoarthritis (KOA) from structural Magnetic Resonance Imaging (MRI) is a difficult task and is greatly subject to user variation. Furthermore, the identification of knee osteoarthritis (KOA) from MRI scans presents a challenge due to the limited information available. A novel methodology using an ensemble Deep Learning algorithm, combining EfficientNet-B3 and ResNext-101 architectures, aims to forecast KOA advancement, bridging the identified gap in clinical trials. The study aims to develop a precise predictive model for knee osteoarthritis using advanced deep-learning architectures and structural MRI scan data. By utilizing an ensemble technique, the model's accuracy in predicting disease development is enhanced, surpassing the limitations of traditional biomarkers. The study used the Osteoarthritis Initiative dataset to develop an ensemble Deep Learning model that combined EfficientNet-B3 and ResNext-101 architectures. Techniques like cropping, gamma correction, and in-slice rotation were used to expand the dataset and improve the model's generalization capacity. The Deep Learning model demonstrated 93% validation accuracy on the OAI dataset, accurately capturing subtle patterns of knee osteoarthritis progression. Augmentation approaches enhanced its resilience. Our ensemble Deep Learning approach, using ResNext-101 and EfficientNet-B3 architectures, accurately predicts knee osteoarthritis courses using structural MRI data, demonstrating the importance of data augmentation for improved predictive tools.

Estradiol associations with brain functional connectivity in postmenopausal women.

Previous studies have found that estrogens play a role in functional connectivity in the brain; however, little research has been done regarding how estradiol is associated with functional connectivity in postmenopausal women. The purpose of this study was to examine the relationship between estradiol and functional connectivity in postmenopausal women. Structural and blood oxygenation level-dependent resting-state magnetic resonance imaging scans of 88 cognitively healthy postmenopausal individuals were obtained along with blood samples collected the same day as the magnetic resonance imaging to assess hormone levels. We generated connectivity values in CONN toolbox version 20.b, an SPM-based software. A regression analysis was run using estradiol level and regions of interest (ROI), including the hippocampus, parahippocampus, dorsolateral prefrontal cortex, and precuneus. Estradiol level was found to enhance parahippocampal gyrus anterior division left functional connectivity during ROI-to-ROI regression analysis. Estradiol enhanced functional connectivity between the parahippocampal gyrus anterior division left and the precuneus as well as the parahippocampal gyrus anterior division left and parahippocampal gyrus posterior division right. An exploratory analysis showed that years since the final menstrual period was related to enhanced connectivity between regions within the frontoparietal network. These results illustrated the relationship between estradiol level and functional connectivity in postmenopausal women. They have implications for understanding how the functioning of the brain changes for individuals after menopause that may eventually lead to changes in cognition and behavior in older ages.

Multimodal integration of neuroimaging and genetic data for the diagnosis of mood disorders based on computer vision models

Mood disorders, particularly major depressive disorder (MDD) and bipolar disorder (BD), are often underdiagnosed, leading to substantial morbidity. Harnessing the potential of emerging methodologies, we propose a novel multimodal fusion approach that integrates patient-oriented brain structural magnetic resonance imaging (sMRI) scans with DNA whole-exome sequencing (WES) data. Multimodal data fusion aims to improve the detection of mood disorders by employing established deep-learning architectures for computer vision and machine-learning strategies. We analyzed brain imaging genetic data of 321 East Asian individuals, including 147 patients with MDD, 78 patients with BD, and 96 healthy controls. We developed and evaluated six fusion models by leveraging common computer vision models in image classification: Vision Transformer (ViT), Inception-V3, and ResNet50, in conjunction with advanced machine-learning techniques (XGBoost and LightGBM) known for high-dimensional data analysis. Model validation was performed using a 10-fold cross-validation. Our ViT ⊕ XGBoost fusion model with MRI scans, genomic Single Nucleotide polymorphism (SNP) data, and unweighted polygenic risk score (PRS) outperformed baseline models, achieving an incremental area under the curve (AUC) of 0.2162 (32.03% increase) and 0.0675 (+8.19%) and incremental accuracy of 0.1455 (+25.14%) and 0.0849 (+13.28%) compared to SNP-only and image-only baseline models, respectively. Our findings highlight the opportunity to refine mood disorder diagnostics by demonstrating the transformative potential of integrating diverse, yet complementary, data modalities and methodologies.

Computational simulation of transcranial magnetic stimulation-induced electric fields in the dorsolateral prefrontal cortex of heavy cannabis using individuals

We aimed to investigate the influence of demographic and clinical modulators on the strength of transcranial magnetic stimulation (TMS)-induced electric fields (EFs) in the left dorsolateral prefrontal cortex (lDLPFC) in heavy cannabis using individuals. Structural T1-weighted magnetic resonance imaging scans of 20 heavy cannabis using individuals and 22 non-cannabis users (the controls) in the age range of 18–25 were retrieved. Computational simulations of TMS-induced EFs in the lDLPFC were performed. No significant difference in the strength of TMS-induced EFs was observed between heavy cannabis using individuals and the controls. A negative correlation between the scalp-to-cortex distance demonstrated and the strength of the induced EFs. The severity of cannabis use related problems did not correlate with the induced EFs in the lDLPFC of heavy cannabis using individuals. However, the severity of alcohol use related problems was negatively correlated with the induced EF in the lDLPFC localized by the 5-cm method in the whole sample. Early adulthood seems related to an increase in the induced EFs in the lDLPFC. In conclusion, the dominant factor influencing TMS-induced EFs was the scalp-to-cortex distance. In early adulthood, the interaction between age and comorbid substance use may influence with the magnitude of TMS-induced EFs, thereby complicating the treatment effect of TMS in young people with substance use disorders.

The effect of age and resilience on the dose-response function between the number of adversity factors and subjective well-being.

Encountering challenges and stress heightens the vulnerability to mental disorders and diminishes well-being. This study explores the impact of psychological resilience in the context of adverse events, considering age-related variations in its influence on well-being. A total of 442 participants (male vs. female =48% vs. 52%) with a mean age of 41.79 ± 16.99 years were collected and completed the following questionnaires Brief Betrayal Trauma Survey (BBTS), Brief Resilience Scale (BRS), Peace of Mind (PoM), The World Health Organization Quality of Life-BREF (WHOQOL-BREF), and Social Support Questionnaire (SSQ). They all underwent structural and resting-state functional magnetic resonance imaging (MRI) scans. Participants were categorized based on adversity levels: 34.39% faced one, 26.24% none, and 19.91, 9.50, and 8.14% encountered two, three, and four adversities, respectively. This categorization helps assess the impact on participants' experiences. As adversity factors increased, PoM decreased. Controlling for age improved PoM model fit (ΔR2 = 0.123, p < 0.001). Adversity factors and age explained 14.6% of PoM variance (df = 2, F = 37.638, p < 0.001). PoM decreased with more adversity and increased with higher age. The study found most participants faced at least one adversity. Adversity negatively affected PoM scores, while resilience acted as a protective factor. Resilience plays a crucial role in buffering the impact of adversities on well-being. Among those with high adversity, higher resilience correlated with stronger DMN-right frontal pole connectivity. Brain volume showed no significant differences, but the quality of life and social support varied between subgroups, with no differences in personal demographic and biophysical features.

Genetic mechanisms underlying local spontaneous brain activity in episodic migraine.

Advances in neuroimaging techniques during the past few decades have captured impaired functional brain activity in migraine disorders, yet the molecular mechanisms accounting for its alterations in migraine remain largely unknown. A total of 27 patients with episodic migraine (EM) and 30 matched healthy controls (HCs) underwent resting-state functional and structural magnetic resonance imaging (MRI) scans. Regional homogeneity (ReHo), low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) of fMRI were compared between the two groups. Based on the Allen Human Brain Atlas and risk genes in migraine, we identified gene expression profiles associated with ReHo alterations in EM. Compared with HCs, patients with EM showed increased ReHo in the left orbital part of the superior frontal gyrus (P < 0.05, cluster-level FWE-corrected). The expression profiles of 16 genes were significantly correlated with ReHo alterations in EM (P < 0.05/5,013, Bonferroni corrected). These genes were mainly enriched for transcription regulation, synaptic transmission, energy metabolism, and migraine disorders. Furthermore, the neural activation was positively correlated with Hamilton Rating Scale for Anxiety (HAMA) scores. To test the stability of our results, we repeated our procedure by using ALFF and fALFF and found these results had a high degree of consistency. Overall, these findings not only demonstrated that regional brain activity was increased in patients with EM, which was associated with emotional regulation but also provided new insights into the genetic mechanisms underlying these changes in migraine.

Development of Efficient Brain Age Estimation Method Based on Regional Brain Volume From Structural Magnetic Resonance Imaging.

We aimed to create an efficient and valid predicting model which can estimate individuals' brain age by quantifying their regional brain volumes. A total of 2,560 structural brain magnetic resonance imaging (MRI) scans, along with demographic and clinical data, were obtained. Pretrained deep-learning models were employed to automatically segment the MRI data, which enabled fast calculation of regional brain volumes. Brain age gaps for each subject were estimated using volumetric values from predefined 12 regions of interest (ROIs): bilateral frontal, parietal, occipital, and temporal lobes, as well as bilateral hippocampus and lateral ventricles. A larger weight was given to the ROIs having a larger mean volumetric difference between the cognitively unimpaired (CU) and cognitively impaired group including mild cognitive impairment (MCI), and dementia groups. The brain age was predicted by adding or subtracting the brain age gap to the chronological age according to the presence or absence of the atrophy region. The study showed significant differences in brain age gaps among CU, MCI, and dementia groups. Furthermore, the brain age gaps exhibited significant correlations with education level and measures of cognitive function, including the clinical dementia rating sum-of-boxes and the Korean version of the Mini-Mental State Examination. The brain age that we developed enabled fast and efficient brain age calculations, and it also reflected individual's cognitive function and cognitive reserve. Thus, our study suggested that the brain age might be an important marker of brain health that can be used effectively in real clinical settings.

Semiautomated electric source imaging determines epileptogenicity of encephaloceles in temporal lobe epilepsy.

We aimed to assess the ability of semiautomated electric source imaging (ESI) from long-term video-electroencephalographic (EEG) monitoring (LTM) to determine the epileptogenicity of temporopolar encephaloceles (TEs) in patients with temporal lobe epilepsy. We conducted a retrospective study involving 32 temporal lobe epilepsy patients with TEs as potentially epileptogenic lesions in structural magnetic resonance imaging scans. Findings were validated through invasive intracerebral stereo-EEG in six of 32 patients and postsurgical outcome after tailored resection of the TE in 17 of 32 patients. LTM (mean duration = 6 days) was performed using the 10/20 system with additional T1/T2 for all patients and sphenoidal electrodes in 23 of 32 patients. Semiautomated detection and clustering of interictal epileptiform discharges (IEDs) were carried out to create IED types. ESI was performed on the averages of the two most frequent IED types per patient, utilizing individual head models, and two independent inverse methods (sLORETA [standardized low-resolution brain electromagnetic tomography], MUSIC [multiple signal classification]). ESI maxima concordance and propagation in spatial relation to TEs were quantified for sources with good signal quality (signal-to-noise ratio > 2, explained signal > 60%). ESI maxima correctly colocalized with a TE in 20 of 32 patients (62.5%) either at the onset or half-rising flank of at least one IED type per patient. ESI maxima showed propagation from the temporal pole to other temporal or extratemporal regions in 14 of 32 patients (44%), confirming propagation originating in the area of the TE. The findings from both inverse methods validated each other in 14 of 20 patients (70%), and sphenoidal electrodes exhibited the highest signal amplitudes in 17 of 23 patients (74%). The concordance of ESI with the TE predicted a seizure-free postsurgical outcome (Engel I vs. >I) with a diagnostic odds ratio of 2.1. Semiautomated ESI from LTM often successfully identifies the epileptogenicity of TEs and the IED onset zone within the area of the TEs. Additionally, it shows potential predictive power for postsurgical outcomes in these patients.

The role of attitudes towards contradiction in psychological resilience: the cortical mechanism of conflicting resolution networks

Managing contradictions and building resilience help us overcome life's challenges. Here, we explored the link between attitudes towards contradictions and psychological resilience, examining the role of cortical conflict resolution networks. We enlisted 173 healthy young adults and used questionnaires to evaluate their cognitive thinking styles and resilience. They underwent structural and functional magnetic resonance imaging scans. Our results revealed that contrasting attitudes toward contradictions, formal logic, and naïve dialecticism thinking styles corresponded with varying degrees of resilience. We noted structural and functional differences in brain networks related to conflict resolution, including the inferior frontal and parietal cortices. The volumetric variations within cortical networks indicated right-hemispheric lateralization in different thinking styles. These findings highlight the potential links between conflict resolution and resilience in the frontoparietal network. We underscore the importance of frontoparietal brain networks for executive control in resolving conflicting information and regulating the impact of contradictions on psychological resilience.

Brain connectivity for subtypes of parkinson's disease using structural MRI.

Objective. Delineating Parkinson's disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson's patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step.Approach. Source-based-morphometry decomposition was performed on 131 Parkinson's patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics.Main results. Two distinct Parkinson's subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree.Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson's datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson's disease may provide subtype-specific treatment.

A deep learning model for brain age prediction using minimally preprocessed T1w images as input.

In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. We used a multicohort dataset of cognitively healthy individuals (age range = 32.0-95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2-4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset's images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1-4, respectively. The model's performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63-2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.

Prenatal paternal anxiety symptoms predict child DHEA levels and internalizing symptoms during adrenarche.

This study examined (1) whether measures of paternal anxious and depressive symptoms collected prenatally and during a follow-up assessment when the child was in middle childhood, predict child neuroendocrine outcomes, and (2) whether neuroendocrine outcomes are intermediate factors between paternal mental health and child cognitive/behavioral outcomes. Middle childhood coincides with increased autonomy as the child transitions into grade school, and with adrenarche, as the maturing adrenal gland increases secretion of dehydroepiandrosterone (DHEA) and its sulfated metabolite (DHEA-S), hormones that are implicated in corticolimbic development which regulate emotions and cognition. Participants were recruited from a subsample of a large prospective birth cohort study (3D study). We conducted a follow-up study when children were 6-8 years old (N = 61 families, 36 boys, 25 girls). Parental symptoms of anxiety, stress and depression were assessed via validated self-report questionnaires: prenatally using an in-house anxiety questionnaire, the Perceived Stress Scale (PSS) and the Center for Epidemiologic Studies Depression (CES-D), and at the follow up, using the Beck Anxiety and Beck Depression Inventories. Children provided salivary hormone samples, and their pituitary gland volume was measured from structural Magnetic Resonance Imaging (MRI) scans. Child behaviors were measured using the Strengths and Difficulties Questionnaire and cognitive outcomes using the WISC-V. Multiple regression analyses were used to test whether paternal mental health symptoms assessed prenatally and during childhood are associated with child neuroendocrine outcomes, adjusting for maternal mental health and child sex. Indirect-effect models assessed whether neuroendocrine factors are important intermediates that link paternal mental health and cognitive/behavioral outcomes. (1) Fathers' prenatal anxiety symptoms predicted lower DHEA levels in the children, but not pituitary volume. (2) Higher prenatal paternal anxiety symptoms predicted higher child internalizing symptoms via an indirect pathway of lower child DHEA. No associations were detected between paternal anxiety symptoms measured in childhood, and neuroendocrine outcomes. No child sex differences were detected on any measure. These results highlight the often-overlooked role of paternal factors during pregnancy on child development, suggesting that paternal prenatal anxiety symptoms are associated with child neuroendocrine function and in turn internalizing symptoms that manifest at least up to middle childhood.

Cerebellar atrophy in pathological subtypes of Alzheimer’s disease and related dementias

AbstractBackgroundThe cerebellum is differentially affected in various neurodegenerative diseases (Gellersen et al., 2017), but associations of cerebellar atrophy with specific neuropathologies have not been systematically analyzed. We used brain imaging to examine cerebellar grey matter volume with patients with all major pathological subtypes of Alzheimer’s disease and related dementias (ADRD).MethodWe applied voxel‐based morphometry to the earliest structural magnetic resonance imaging scans of autopsy‐proven ADRD cases (n = 309), and age‐, sex‐, and education‐matched healthy controls (n = 80). Seventy‐seven AD cases, 74 cases with mixed Lewy body disease and AD (LBD‐AD), and 158 frontotemporal lobar degeneration (FTLD) cases were included in this study. Patients with FTLD were further classified into FTLD major molecular classes and subtypes: FTLD‐TDP [FTLD‐TDP type A (TDP‐A) (n = 21), FTLD‐TDP type B (TDP‐B) (n = 21), FTLD‐TDP type C (TDP‐C) (n = 26)] and FTLD‐tau [Pick’s disease (n = 25), corticobasal degeneration (CBD) (n = 32), progressive supranuclear palsy (PSP) (n = 33)]. Clinical function was assessed with the Clinical Dementia Rating Scale (CDR) (Morris, 1993; Hughes et al., 1982) at the time of MRI scan, with global score ranging from 0 (normal), 0.5 (very mildly impaired), 1 (mildly impaired), 2 (moderately impaired) to 3 (severely impaired).ResultsCompared with controls, distinct patterns of cerebellar atrophy were observed in all pathological subtypes spanning bilateral hemispheres and vermis (PFWE‐corr&lt;0.05, &gt;100 contiguous voxels). Significant cerebellar grey matter changes were even found in the early stage (CDR ≦ 1) of all subtypes, with the exception of TDP‐B, and in the very early stage (CDR 0‐0.5) of AD, LBD‐AD, TDP‐A and PSP. Degree of cortical atrophy positively predicted degree of cerebellar atrophy in all subtypes.ConclusionThis study is the first to identify distinct early patterns of cerebellar atrophy across pathological subtypes of ADRD, and to show a direct relationship between cerebellar and cortical grey matter volume. These results clarify the cerebellar involvement in ADRD and underscore the potential for cerebellar neuroimaging to be a non‐invasive biomarker for differential diagnosis among pathologies and disease monitoring.

Connectome-based predictive modelling can predict follow-up craving after abstinence in individuals with opioid use disorders

BackgroundIndividual differences have been detected in individuals with opioid use disorders (OUD) in rehabilitation following protracted abstinence. Recent studies suggested that prediction models were effective for individual-level prognosis based on...

Volume changes of the subcortical limbic structures in major depressive disorder patients with and without anhedonia

Anhedonia is a core feature of major depressive disorder (MDD) and the limbic system has been indicated to be associated with anhedonia in MDD due to its crucial role within the reward circuit. However, the relationship between different regions of the limbic system and MDD, particularly anhedonic symptoms, remains unclear. Therefore, the purpose of this study was to investigate volume changes of various parts of the subcortical limbic (ScLimbic) system in MDD with and without anhedonia. A total of 120 individuals, including 30 MDD patients with anhedonia, 43 MDD patients without anhedonia, and 47 healthy controls (HCs) were enrolled in this study. All subjects underwent structural magnetic resonance imaging scans. After that, ScLimbic system segmentation was performed using the FreeSurfer pipeline ScLimbic. Analysis of covariance (ANCOVA) was performed to identify brain regions with significant volume differences among three groups, and then, post hoc tests were calculated for inter-group comparisons. Finally, correlations between volumes of different parts of the ScLimbic and clinical characteristics in MDD patients were further analyzed. The ANCOVA revealed significant volume differences of the ScLimbic system among three groups in the bilateral fornix (Fx), and the right basal forebrain (BF). As compared with HCs, both groups of MDD patients showed decreased volume in the right Fx, meanwhile, MDD patients with anhedonia further exhibited volume reductions in the left Fx and right BF. However, no significant difference was found between MDD patients with and without anhedonia. No significant association was observed between subregion volumes of the ScLimbic system and clinical features in MDD. The present findings demonstrated that MDD patients with and without anhedonia exhibited segregated brain structural alterations in the ScLimbic system and volume loss of the ScLimbic system might be fairly extensive in MDD patients with anhedonia.

Clinical and biological relevance of glial fibrillary acidic protein in Alzheimer’s disease

IntroductionThere is a tremendous need for identifying reliable blood-based biomarkers for Alzheimer’s disease (AD) that are tied to the biological ATN (amyloid, tau and neurodegeneration) framework as well as clinical assessment and progression.MethodsOne hundred forty-four elderly participants underwent 18F-AV45 positron emission tomography (PET) scan, structural magnetic resonance imaging (MRI) scan, and blood sample collection. The composite standardized uptake value ratio (SUVR) was derived from 18F-AV45 PET to assess brain amyloid burden, and the hippocampal volume was determined from structural MRI scans. Plasma glial fibrillary acidic protein (GFAP), phosphorylated tau-181 (ptau-181), and neurofilament light (NfL) measured by single molecular array (SIMOA) technology were assessed with respect to ATN framework, genetic risk factor, age, clinical assessment, and future functional decline among the participants.ResultsAmong the three plasma markers, GFAP best discriminated participants stratified by clinical diagnosis and brain amyloid status. Age was strongly associated with NfL, followed by GFAP and ptau-181 at much weaker extent. Brain amyloid was strongly associated with plasma GFAP and ptau-181 and to a lesser extent with plasma NfL. Moderate association was observed between plasma markers. Hippocampal volume was weakly associated with all three markers. Elevated GFAP and ptau-181 were associated with worse cognition, and plasma GFAP was the most predictive of future functional decline. Combining GFAP and ptau-181 together was the best model to predict brain amyloid status across all participants (AUC = 0.86) or within cognitively impaired participants (AUC = 0.93); adding NfL as an additional predictor only had a marginal improvement.ConclusionOur findings indicate that GFAP is of potential clinical utility in screening amyloid pathology and predicting future cognitive decline. GFAP, NfL, and ptau-181 were moderately associated with each other, with discrepant relevance to age, sex, and AD genetic risk, suggesting their relevant but differential roles for AD assessment. The combination of GFAP with ptau-181 provides an accurate model to predict brain amyloid status, with the superior performance of GFAP over ptau-181 when the prediction is limited to cognitively impaired participants.

Healthy brain aging and delayed dementia in Texas rural elderly.

Healthy aging is the process of preserving and enhancing one's independence, physical and mental well-being, and overall quality of life. It involves the mental, emotional, and cognitive wellness. Although biological and genetic factors have a significant influence on the process of aging gracefully, other adjustable factors also play a crucial role. Adopting positive behaviors such as maintaining a nutritious and balanced diet, engaging in regular physical activity, effectively managing stress and anxiety, ensuring sufficient sleep, nurturing spiritual coping mechanisms, and prioritizing overall well-being from an early stage can collectively influence both lifespan and the quality of health during advanced years. We aim to explore the potential impacts of biological, psychosocial, and environmental factors on the process of healthy cognitive aging in individuals who exhibit healthy aging. Additionally, we plan to present initial findings that demonstrate how maintaining good cognitive health during aging could potentially postpone the emergence of neurodegenerative disorders. We hypothesize that there will be strong associations between biological, environmental, and social factors that cause some elderly to be superior in cognitive health than others. For preliminary data collection, we recruited 25 cognitively healthy individuals and 5 individuals with MCI/AD between the ages of 60-90 years. We conducted anthropometric measurements, and blood biomarker testing, administered surveys, and obtained structural brain magnetic resonance imaging (MRI) scans. The Montreal Cognitive Assessment (MoCA) scores and sub-scores for the healthy group were also reported. We found that at baseline, individuals exhibiting healthy cognitive aging, and those with MCI/AD had comparable measures of anthropometrics and blood biomarkers. The healthy group exhibited lower signs of brain volume loss and the ones observed were age-related. Moreover, within the healthy group, there was a significant correlation (p=0.003) between age and MoCA scores. Conversely, within the individuals with MCI/AD, the MRI scans showed disease signs of grey and white matter and loss of cerebral volume. Healthy brain aging is a scientific area that remains under-explored. Our current study findings support our hypothesis. Future studies are required in diverse populations to determine the various biological, psychological, environmental, lifestyle, and social factors that contribute to it.

Single-subject cortical morphological brain networks: Phenotypic associations and neurobiological substrates

Although single-subject morphological brain networks provide an important way for human connectome studies, their roles and origins are poorly understood. Combining cross-sectional and repeated structural magnetic resonance imaging scans from adults, children and twins with behavioral and cognitive measures and brain-wide transcriptomic, cytoarchitectonic and chemoarchitectonic data, this study examined phenotypic associations and neurobiological substrates of single-subject morphological brain networks. We found that single-subject morphological brain networks explained inter-individual variance and predicted individual outcomes in Motor and Cognition domains, and distinguished individuals from each other. The performance can be further improved by integrating different morphological indices for network construction. Low-moderate heritability was observed for single-subject morphological brain networks with the highest heritability for sulcal depth-derived networks and higher heritability for inter-module connections. Furthermore, differential roles of genetic, cytoarchitectonic and chemoarchitectonic factors were observed for single-subject morphological brain networks. Cortical thickness-derived networks were related to the three factors with contributions from genes enriched in membrane and transport related functions, genes preferentially located in supragranular and granular layers, overall thickness in the molecular layer and thickness of wall in the infragranular layers, and metabotropic glutamate receptor 5 and dopamine transporter; fractal dimension-, gyrification index- and sulcal depth-derived networks were only associated with the chemoarchitectonic factor with contributions from different sets of neurotransmitter receptors. Most results were reproducible across different parcellation schemes and datasets. Altogether, this study demonstrates phenotypic associations and neurobiological substrates of single-subject morphological brain networks, which provide intermediate endophenotypes to link molecular and cellular architecture and behavior and cognition.