Structural Brain MRI Research Articles

Automated quality control of structural brain MRI scans from aging and dementia datasets

AbstractBackgroundDementias platform UK (DPUK) currently hosts over 40 dementia cohorts and provides a remote analysis environment for multimodal cross‐cohort data analysis. Nineteen cohorts include brain MRI data, hence automated quality control (QC) becomes crucial for the reliability of image analyses. This work aims to provide a framework for quality prediction of T1‐weighted (T1w) scans from aging and dementia datasets: we compared manual quality ratings with the output of two existing automated QC pipelines and propose a new QC classifier.MethodWe used 2438 T1w scans (including patients and controls, 1.5T and 3T scanners, 3 manufacturers, 11 sites) from 4 different aging and dementia datasets (ADNI, Whitehall II, Oxford Parkinson’s Disease Centre, Oxford Brain Health Clinic). Manual quality ratings for all images were performed by the dataset owners. Each image was processed in MRIQC (Esteban et al. 2017, PLoS ONE) and CAT12 (Computational Anatomy Toolbox, Gaser et al. 2022, bioRxiv). We compared the quality agreement for MRIQC predictions and CAT12’s weighted image quality ratings (IQR) with manual QC using inter‐rater reliability. Further, we explored the effect of changing the accept‐reject threshold from automated QC tools on the reliability measure. Finally, we designed a custom QC classifier (manual QC as target) by combining MRIQC and CAT12’s quality metrics as features and training linear support vector machines in a nested cross‐validation framework.ResultWe found overall agreement between automated QC methods and manual QC when applied to dementia datasets (MRIQC: Kappa = 0.30, p = 0.001; CAT12: Kappa = 0.28, p = 0.001). Adjusting the acceptance thresholds of the automated tools within dataset improved the agreement. On the combined data (training N = 1951; test N = 487), the proposed classifier showed 94.2% accuracy (sensitivity 94.3%, specificity 85.7%) on the test data. Leaving one site out (training with 10 sites, N = 2055), we found 86.4% accuracy (sensitivity 86.2%, specificity 100%) on the test site (N = 383).ConclusionThe performance of the proposed classifier appears promising on these heterogeneous datasets from different scanners. In the future, we aim to utilise this framework for improving generalisability of prediction and release the classifier on the DPUK data portal to robustly QC T1w scans for other aging and dementia cohorts.

Does biological sex moderate the association of nature exposure with brain volume? A cross‐sectional analysis of the UK Biobank

AbstractBackgroundNeighbourhood nature spaces are associated with brain health. Biological sex moderates the associations of various lifestyle and environmental factors with brain health, but it is unclear whether sex moderates associations between nature exposure and brain structure. We thus investigated whether sex moderates the association of residential nature exposure with brain structure in the UK Biobank cohort.MethodsWe conducted a cross‐sectional analysis of the UK Biobank study. We included cognitively healthy participants with available nature exposure data at study entry (2006‐2010) who completed a 3T structural brain MRI (2014‐2021; N = 37,880). Residential nature exposure was estimated as the percentage of land classified as natural environment within 1000m of a participant’s home. We indexed total grey matter volume and total white matter volume, normalized for head size, and hippocampal volume averaged from both hemispheres. We performed linear regressions to assess the independent associations of residential nature exposure and biological sex with brain structure. We then assessed whether sex moderated the association between residential nature exposure and brain structure, and plotted simple slopes to interpret interaction effects. All models adjusted for age, body mass index (kg/m2), educational attainment, income, physical activity (min/day), time spent outdoors (hours/day), smoking status, disability status, Townsend deprivation index, and population density.ResultsAt study entry, mean age was 55 years (SD = 7.53 years) and 53% of participants were female. Average residential nature exposure was 44% (SD = 26%). Every 10% increment in residential nature exposure was associated with 737 mm3 greater grey matter volume (p<0.0001) and 601 mm3 greater white matter volume (p<0.0001). Females had 24,270 mm3 greater grey matter volume (p<0.0001), 9,703 mm3 less white matter volume (p<0.0001), and 223 mm3 less hippocampal volume than males (p<0.0001). Sex moderated the association of residential nature exposure with total grey matter volume, such that every 10% increment in nature exposure attenuated sex differences in grey matter volume by 422 mm3 (p = 0.020) (Figure 1).ConclusionThese results suggest that neighbourhood nature spaces may benefit brain health, and that males may be more sensitive than females to these potential benefits. Longitudinal analyses are needed to verify these findings.

Associations among depressive symptoms, hippocampal volume and cognition in community‐dwelling older Latinos

AbstractBackgroundLatinos are the fastest growing subpopulation of older adults in the U.S. and have a higher risk for dementia than non‐Latino Whites. Depressive symptoms are prevalent among US Latinos. However, these symptoms often go undiagnosed and are undertreated. Depressive symptoms may be early manifestations of Alzheimer’s Disease (AD) before the onset of mild cognitive impairment and dementia. It’s unclear whether depressive symptoms in the presence of incipient neurodegeneration are associated with cognitive function in older Latino adults. Here, we examined the associations among depressive symptoms, cognition and hippocampal volume in older Latinos.MethodA total of 65 Spanish‐speaking Latinos enrolled in the Boston Latino Aging Study (BLAST) and the Harvard Aging Brain Study (HABS) were included. Depressive symptoms were assessed with the 30‐item‐Geriatric Depression Scale (GDS) and cognition with the Preclinical Alzheimer’s Cognitive Composite (PACC5), which includes the Mini‐Mental State Examination (MMSE), Digit Symbol Coding, Free and Cued Selective Reminding Test, Category Fluency (animals) and Logical Memory Delayed Recall (NEUROPSI Stories for BLAST participants). A subset of 34 participants also underwent a structural brain MRI on a 3T scanner. Spearman correlations were used to determine cross‐sectional associations among depressive symptoms, hippocampal volume, and cognition. Models were adjusted for age and education.ResultsParticipants had a mean age of 66 +/‐ 7.2 years, a mean education of 12.9 +/‐ 5.1 years, and 74% were female. They had a mean MMSE score of 26.7 +/‐ 2.8, a mean GDS score of 6.1 +/‐ 5.5 and a mean adjusted hippocampal volume of 7836.5 +/ = 630.9 mm3. Correlation analyses revealed that higher depressive symptoms were associated with lower years of education (r = ‐0.39, p<0.001), worse performance on cognitive measures (r = ‐0.33, p = 0.009) and smaller hippocampal volume (r = ‐0.61, p = <0.001).ConclusionsPreliminary results of this ongoing biomarker study suggest that depressive symptoms in older Latinos are associated with markers of neurodegeneration (i.e., hippocampal volume) and worse cognition. Future work will focus on investigating predictors of long‐term emotional and cognitive functioning in Spanish‐speaking, older Latino populations.

A slice selection guided deep integrated pipeline for Alzheimer’s prediction from Structural Brain MRI

Alzheimer’s disease, a progressive form of dementia, has risen to become the fifth leading cause of death among individuals aged 65 and older. The diagnosis of Alzheimer’s is both time-consuming and costly, involving radiologists and clinical experts at multiple stages, which presents a significant challenge in the medical field. Moreover, cases of Alzheimer’s and dementia often go undiagnosed or misdiagnosed worldwide. To address this issue, medical experts meticulously analyze patients’ structural MRI (sMRI) scans to identify potential abnormalities linked to Alzheimer’s or other forms of dementia. Recognizing the devastating impact of this disease on people’s lives, Artificial Intelligence (AI) researchers have been dedicated to developing automated solutions for early-stage Alzheimer’s diagnosis in recent years, aiming to support medical practitioners in their efforts. Despite the application of various AI-driven solutions that use sMRI data for Alzheimer’s diagnosis, there are still research gaps that need attention. These gaps include the need for guided slice selection and the development of a simpler yet effective integrated pipeline where each stage of the process is fully automated, eliminating the need for medical practitioner intervention. In this study, we propose an integrated automated solution that incorporates a guided machine learning-based selection process using K-Means++ leading to a Gradient Boosting-based method for identifying the 16 most relevant 2-dimensional sMRI slices from 3-dimensional sMRI data. This step is crucial for accurate Alzheimer’s classification. Furthermore, we introduce a deep learning architecture that combines EfficientNetV2S-based transfer learning with densely-learned features in an optimized manner. To evaluate the effectiveness of our proposed deep-integrated architecture, we used two benchmark datasets from ADNI and OASIS, conducting rigorous experimental analysis and validation. The results demonstrated that our integrated architecture outperformed all other experimented architectures, achieving a 20-Fold Cross Validation Accuracy of 83.64% (CN vs AD), 82.69% (CN vs MCIc), and 71.40% (CN vs MCInc) on the ADNI dataset, and 91.54% (CN vs AD) on the OASIS dataset. This signifies the potential of our approach in improving Alzheimer’s diagnosis accuracy and offers hope for early detection and intervention in this debilitating disease.

Accelerometer-Measured Sedentary Behavior Patterns, Brain Structure, and Cognitive Function in Dementia-Free Older Adults: A Population-Based Study.

Sedentary behavior is associated with cognitive impairment, but the neuropathological mechanisms underlying their associations are poorly understood. To investigate the associations of accelerometer-measured sedentary behavior patterns with brain structure and cognition, and further to explore the potential mechanisms. This community-based study included 2,019 older adults (age≥60 years, 59% women) without dementia derived from participants in the baseline examination of MIND-China (2018-2020). We assessed sedentary parameters using an accelerometer and cognitive function using a neuropsychological test battery. Structural brain markers were assessed on the structural brain MRI scans in a subsample (n = 1,009). Data were analyzed using the general linear, isotemporal substitution, and mediation models. In the total sample (n = 2,019), adjusting for multiple covariates and moderate-to-vigorous-intensity physical activity, longer mean sedentary bout duration was linearly related with lower z-scores of global cognition, verbal fluency, and memory (ptrend < 0.05), whereas greater total sedentary time was linearly associated with lower z-scores of global cognition, verbal fluency, and memory only among individuals with long sedentary time (>10 h/day) (ptrend < 0.05); Breaking up sedentary time with same amount of light-intensity physical activity was significantly associated with higher verbal fluency and memory z-scores (p < 0.05). In the MRI subsample (n = 1,009), separately entering structural brain MRI markers into the mediation models substantially attenuated the associations of mean sedentary bout duration with global cognition, verbal fluency, and memory z-scores. Prolonged uninterrupted sedentary time is associated with poor global cognition, memory, and verbal fluency among rural older adults, and structural brain markers could partially mediate the association.

Sleep, brain systems, and persistent stress in early adolescents during COVID-19: Insights from the ABCD study

PurposeThe first year of the COVID-19 pandemic constituted a major life stress event for many adolescents, associated with disrupted school, behaviors, social networks, and health concerns. However, pandemic-related stress was not equivalent for everyone and could have been influenced by pre-pandemic factors including brain structure and sleep, which both undergo substantial development during adolescence. Here, we analyzed clusters of perceived stress levels across the pandemic and determined developmentally relevant pre-pandemic risk factors in brain structure and sleep of persistently high stress during the first year of the COVID-19 pandemic. MethodsWe investigated longitudinal changes in perceived stress at six timepoints across the first year of the pandemic (May 2020–March 2021) in 5559 adolescents (50 % female; age range: 11–14 years) in the United States (U.S.) participating in the Adolescent Brain Cognitive Development (ABCD) study. In 3141 of these adolescents, we fitted machine learning models to identify the most important pre-pandemic predictors from structural MRI brain measures and self-reported sleep data that were associated with persistently high stress across the first year of the pandemic. ResultsPatterns of perceived stress levels varied across the pandemic, with 5 % reporting persistently high stress. Our classifiers accurately detected persistently high stress (AUC > 0.7). Pre-pandemic brain structure, specifically cortical volume in temporal regions, and cortical thickness in multiple parietal and occipital regions, predicted persistent stress. Pre-pandemic sleep difficulties and short sleep duration were also strong predictors of persistent stress, along with more advanced pubertal stage. ConclusionsAdolescents showed variable stress responses during the first year of the COVID-19 pandemic, and some reported persistently high stress across the whole first year. Vulnerability to persistent stress was evident in several brain structural and self-reported sleep measures, collected before the pandemic, suggesting the relevance of other pre-existing individual factors beyond pandemic-related factors, for persistently high stress responses.

97 Exploring Urban-Rural Disparities in Alzheimer’s disease: Clinical characterization of a southern Nevada cohort

Objective:As the US population ages, the prevalence of Alzheimer’s disease and related dementias (AD/RD) is on the rise. This is especially true in rural America, where mortality rates due to AD/RD are rising faster than in metropolitan areas. To date, however, people living in rural communities are severely underrepresented in aging research. The Nevada Exploratory Alzheimer’s Disease Research Center (NVeADRC) seeks to address this gap. Here, we present preliminary cognitive data from our rural-dwelling cohort, as well as relevant demographic and clinical characteristics.Participants and Methods:Individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer’s disease (AD) living in rural communities, defined as a rural-urban commuting area (RUCA) code of 4 or higher, were enrolled through either clinic or community outreach. Eligibility for the observational cohort required: age &gt;55 years, primarily English-speaking, primary residence in a rural community, and availability of a study partner. Measures included the Uniform Data Set (v3), blood-based biomarkers, structural brain MRI, and portions of the PhenX Social Determinants of Health toolkit. Participants are seen at baseline and followed annually, with interim remote visits every 6 months. A multidisciplinary consensus diagnosis is rendered after each visit. Where feasible, a harmonized urban cohort followed by the Nevada Center for Neurodegeneration and Translational Neuroscience (CNTN) was used for comparison.Results:Fifty-six rural-dwelling (age=70.4±7.1 years; edu=15.2±2.6 years; 61% female) and 148 urban-dwelling (age=72.9±6.8 years; edu=15.8±2.7 years; 46% female) older adults were included; age significantly differed between cohorts but education did not. The rural cohort was 46% NC (MoCA=26.8±2.3; CDRsob=0.3±0.6), 32% MCI (MoCA=22.8±3.1; CDRsob=1.2±1.0), and 22% AD (MoCA=16.9±5.5; CDRsob=5.2±3.0). The urban cohort was 39% NC (MoCA=26.4±2.6; CDRsob=0.3±0.8), 44% MCI (MoCA=22.3±3.1; CDRsob=2.0±1.5) and 17% AD (MoCA=18.6±3.9; CDRsob=4.7±2.3). Rural communities were significantly more disadvantaged, as measured by the Area Deprivation Index (ADI), than urban communities (rural ADI=6.3±2.6; urban ADI=3.4±2.3; p&lt;.001). Fifty-percent of the rural cohort lives in a moderate to severely disadvantaged neighborhood (ADI Decile&gt;7) compared to 12% of the urban cohort, and 11% of individuals in the rural cohort reported living more than 30 miles from the nearest medical facility. Across the combined cohort, education was significantly correlated with ADI deciles (r=-.30, p&lt;.001), with people in the areas of highest disadvantage having the lowest education. Verbal memory was also inversely associated with ADI. There were no differences in clinical diagnosis as a function of ADI rank.Conclusions:Living in a rural community conveys a multifaceted array of risks and benefits, some of which differ from urban settings. The literature to date suggests that older adults living in rural communities are at significantly increased risk for morbidity and mortality due to AD/RD, though it is unclear why. Preliminary data from the NVeADRC show that increasing levels of neighborhood disadvantage were associated with lower levels of education and worse verbal memory in this convenience sample. The combined effect of low education and increased disadvantage account for some of the urban-rural differences in mortality that have been reported, though additional research on representative samples in this underrepresented population is critical.

74 A Phase I Trial of Accelerated, HighDose Repetitive Transcranial Magnetic Stimulation to Improve Cognition in Amnestic MCI

Objective:Repetitive transcranial magnetic stimulation (rTMS), a non-invasive neuromodulation therapy most widely used in depression, has shown evidence of secondary benefits for cognition in both neurologic and neuropsychiatric conditions. The recent development of more efficient stimulation protocols, such as accelerated high-dose intermittent theta burst (iTBS)-rTMS, has substantially reduced treatment burden by shortening the treatment course by &gt;50%. This study aimed to establish the safety, feasibility, acceptability, and preliminary efficacy of iTBS-rTMS as a tool for bolstering cognition in individuals with amnestic mild cognitive impairment (aMCI).Participants and Methods:Twenty-four patients with aMCI were enrolled in an open-label phase I trial of iTBS-rTMS; 2 withdrew prior to initiating treatment due to personal circumstances. All participants had received a diagnosis of MCI due to possible AD from a healthcare provider (i.e., neurologist or neuropsychologist) and met actuarial neuropsychological criteria for aMCI. This sample of older adults (range: 61.5-85.2 years, M = 74.1, SD = 5.71) was predominantly White/non-Hispanic (n = 23; Black/non-Hispanic: n = 1), roughly half female (n = 13), with a college education (range: 12-20 years, M = 15.9, SD = 2.5). Participants received 24 sessions of iTBS-rTMS to the left dorsolateral prefrontal cortex over 3 days (8 sessions each, lasting roughly 2 hours per day). Participants rated their perceptions and experience of common side effects during and after each treatment session as well as retrospectively at post-treatment and 4-week follow-up. They completed structural and functional brain MRI, neuropsychiatric evaluations, and neuropsychological assessments before and after treatment and were administered a subset of these measures at 4-week follow-up. MoCA scores were used to monitor for adverse neurocognitive effects, and the fluid cognition composite score from the NIH Toolbox Cognition Battery was used to test preliminary efficacy.Results:We achieved a high retention rate (95%), with 21 of the 22 participants completing all study procedures. There were no clinically significant adverse neuroradiological, neuropsychiatric, or neurocognitive effects of treatment. Participant reports indicated high tolerability and acceptability, with a modal rating of 0 (on a scale from 0=not at all to 10=extremely) for six common side effects (i.e., headache, pain, scalp irritation, facial twitching, fatigue, fear/anxiety), assessed both during and after each treatment session. They reported very low desire to quit despite some participants rating the treatment as moderately tiring. We observed significant, large effect-size (d = 0.98) improvements in fluid cognition from pre- to post-treatment.Conclusions:Our findings support the safety, feasibility, and acceptability of iTBS-rTMS treatment in patients with aMCI. Further, although not explicitly dosed for efficacy, we provide preliminary evidence of improved fluid cognition as a function of treatment, highlighting the potential of this treatment for improving trans-domain cognitive impairment. These promising results can directly inform future trials aimed at optimizing treatment parameters, broadening the indication to other MCI subtypes, and testing the augmentation of established cognitive rehabilitation interventions when combined with rTMS.

Neuroanatomical findings in isolated REM sleep behavior disorder and early Parkinson’s disease: a Voxel-based morphometry study

Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a parasomnia characterized by loss of physiological atonia of skeletal muscles with abnormal behaviors arising during REM sleep. RBD is often the early manifestation of neurodegenerative diseases, particularly alpha-synucleinopathies, such as Parkinson’s disease (PD). Both structural and functional neuroimaging studies suggest that iRBD might share, or even precede, some of the features commonly found in PD, although without a definitive conclusion. Aim of the study is to evaluate the presence of structural abnormalities involving cortical and subcortical areas in PD patients with RBD and iRBD. Patients with video-polysomnographic (VPSG)-confirmed iRBD, and patients with a diagnosis of PD were recruited. In all PD patients, the presence of probable RBD was assessed during the follow-up visits (PD/pRBD). A group of healthy controls (HC) subjects was also recruited. Each subject underwent a structural brain MRI using a 3-D T1-weighted spoiled gradient echo sequence. Twenty-three patients with iRBD, 24 PD/pRBD, and 26 HC were enrolled. Voxel-based morphometry-AnCOVA analysis revealed clusters of grey matter changes in iRBD and PD/pRBD compared to HC in several regions, involving mainly the frontal and temporal regions. The involvement of cortical brain structures associated to the control of sleep cycle and REM stage both in PD/pRBD and iRBD might suggest the presence of a common structural platform linking iRBD and PD, although this pattern may not underlie exclusively RBD-related features. Further longitudinal studies are needed to clarify the patterns of changes occurring at different time points of RBD-related neurodegeneration.

A - 21 Sex Differences in Cortical Thickness and Cognition in Individuals with Autosomal Dominant Alzheimer's Disease.

Cortical thinning is a marker of neurodegeneration. Individuals with autosomal dominant Alzheimer's disease (ADAD) develop early-onset dementia with virtually 100% certainty. Cortical thickness fluctuates across the ADAD lifespan, from early-life increased thickness to atrophy proximal to clinical onset. Females have higher cortical thickness and better cognition than males as they age. We examined sex differences in cortical thickness and cognitive performance in adults with ADAD. We assessed 85 Presenilin-1 E280A mutation carriers (74 cognitively-unimpaired, 11 mildly-impaired) from the Colombia-Boston Biomarker Study of ADAD. Mean age was 35.7 (SD = 10), education 10.2 (SD = 9), and 55 were female. Global cognition was measured using Mini-Mental State Examination (MMSE), and memory using CERAD Word List Delayed Recall. Participants underwent structural brain MRI. T-tests assessed between-group differences. Pearson correlations examined associations between cortical thickness and cognition. There were no sex differences in cortical thickness or cognitive performance (all ps > =0.05). For carriers, greater cortical thickness was associated with higher MMSE (r = 0.40, p < 0.001) and memory recall scores (r = 0.43, p < 0.01). Female carriers drove this effect, where greater cortical thickness was associated with higher MMSE (r = 0.46, p < 0.001) and memory recall scores (r = 0.47, p < 0.001); these associations were non-significant in male carriers alone. In cognitively-unimpaired female carriers, increased cortical thickness was associated with worse MMSE (r = -0.44, p < 0.001). We found sex differences in the relation between cortical thickness and cognition in ADAD carriers, which may be relevant to the differential vulnerability and progression of ad in females and males. Future work should investigate potential underlying mechanisms contributing to this sex difference.

Detecting schizophrenia with 3D structural brain MRI using deep learning

Schizophrenia is a chronic neuropsychiatric disorder that causes distinct structural alterations within the brain. We hypothesize that deep learning applied to a structural neuroimaging dataset could detect disease-related alteration and improve classification and diagnostic accuracy. We tested this hypothesis using a single, widely available, and conventional T1-weighted MRI scan, from which we extracted the 3D whole-brain structure using standard post-processing methods. A deep learning model was then developed, optimized, and evaluated on three open datasets with T1-weighted MRI scans of patients with schizophrenia. Our proposed model outperformed the benchmark model, which was also trained with structural MR images using a 3D CNN architecture. Our model is capable of almost perfectly (area under the ROC curve = 0.987) distinguishing schizophrenia patients from healthy controls on unseen structural MRI scans. Regional analysis localized subcortical regions and ventricles as the most predictive brain regions. Subcortical structures serve a pivotal role in cognitive, affective, and social functions in humans, and structural abnormalities of these regions have been associated with schizophrenia. Our finding corroborates that schizophrenia is associated with widespread alterations in subcortical brain structure and the subcortical structural information provides prominent features in diagnostic classification. Together, these results further demonstrate the potential of deep learning to improve schizophrenia diagnosis and identify its structural neuroimaging signatures from a single, standard T1-weighted brain MRI.

ADHD diagnosis using structural brain MRI and personal characteristic data with machine learning framework.

An essential yet challenging task is an automatic diagnosis of attention-deficit/hyperactivity disorder (ADHD) without manual intervention. The present study emphasises utilizing structural MRI and personal characteristic (PC) data for developing an automated diagnostic system for ADHD classification. Here, an age-balanced dataset of 316 ADHD and 316 Typically Developing Children (TDC) was prepared from the publicly available dataset. We extracted volumetric features from gray matter (GM) volumes from brain regions defined by Automated Anatomical Labelling (AAL3) atlas and cortical thickness-based (CT) features using the Destrieux atlas. A set of salient features were selected independently using minimum redundancy and maximum relevance (mRMR) and ensemble feature selection (EFS) methods. Decision models were trained using five well-known classifiers: K-nearest neighbours, logistic regression, linear Support Vector Machine (SVM), radial-based SVM (RBSVM), and Random Forest. The performance of the proposed system was evaluated using accuracy, recall, and specificity with ten runs of a ten-fold cross-validation scheme. We run seven experiments by considering different combinations of features. The maximum classification accuracy of 75% was obtained with CT and PC features with RBSVM and SVM with the EFS. An increase in GM volume in fifteen brain regions and loss of cortical thickness in twenty-seven brain regions were observed.

Neuroanatomical correlates of cognitive impairment following basal ganglia-thalamic post-hemorrhagic stroke: Uncovering network-wide alterations in hemispheric gray matter asymmetry

Cognitive impairment and recovery are central issues in hemorrhagic stroke. This study aimed to investigate whether post-hemorrhagic stroke cognitive impairment (PhSCI) is associated with cortical gray matter (GM) loss and hemispheric asymmetry changes and whether these changes could predict improvements in cognitive function during the recovery. Nineteen patients with PhSCI, comprising 10 with basal ganglia hemorrhage and 9 with thalamic hemorrhage, were recruited. Among them, 9 completed a course of repetitive transcranial magnetic stimulation (rTMS). Additionally, 19 demographically and comorbidity-matched healthy controls were also included. Structural brain MRI and cognitive assessments were performed. Voxel-wise GM volume and hemispheric asymmetry were analyzed. The PhSCI patients exhibited bilateral, yet asymmetric, GM losses in the hippocampus, fusiform, lateral temporal, prefrontal, somatomotor, and inferior parietal regions. The analysis of GM asymmetry revealed that patients showed rightward GM in the lateral temporal, somatomotor, and inferior parietal regions. Among the 9 PhSCI patients who completed rTMS, there was a marginal trend of regional GM increase and leftward GM, and these changes were in parallel with the improvements in cognitive tests. Further lesion connectivity and metanalytic mapping identified two interconnected systems linked to the lesions, which were anchored in the default mode, somatomotor, and salience/cognitive control networks and in the cognitive domains of memory, language, decision-making, and executive function. In conclusion, PhSCI patients exhibited network-wide cortical GM losses, distal to subcortical hemorrhagic lesions, and hemisphere asymmetry changes. These changes appear to predict rTMS-related cognitive improvements, suggesting that even subcortical focal lesions can lead to alterations in distal cortical neuroanatomical architecture. Our preliminary findings provide new insights into the neuroanatomical basis of PhSCI.

Shanghai Cognitive Screening: A Mobile Cognitive Assessment Tool Using Voice Recognition to Detect Mild Cognitive Impairment and Dementia in the Community.

A rapid digital instrument is needed to facilitate community-based screening of mild cognitive impairment (MCI) and Alzheimer's disease (AD) in China. We developed a voice recognition-based cognitive assessment (Shanghai Cognitive Screening, SCS) on mobile devices and evaluated its diagnostic performance. Participants (N = 251) including healthy controls (N = 98), subjective cognitive decline (SCD, N = 42), MCI (N = 80), and mild AD (N = 31) were recruited from the memory clinic at Shanghai Sixth People's Hospital. The SCS is fully self-administered, takes about six minutes and measures the function of visual memory, language, and executive function. Participants were instructed to complete SCS tests, gold-standard neuropsychological tests and standardized structural 3T brain MRI. The Cronbach's alpha was 0.910 of the overall scale, indicating high internal consistency. The SCS total score had an AUC of 0.921 to detect AD (sensitivity = 0.903, specificity = 0.945, positive predictive value = 0.700, negative predictive value = 0.986, likelihood ratio = 16.42, number needed for screening utility = 0.639), and an AUC of 0.838 to detect MCI (sensitivity = 0.793, specificity = 0.671, positive predictive value = 0.657, negative predictive value = 0.803, likelihood ratio = 2.41, number needed for screening utility = 0.944). The subtests demonstrated moderate to high correlations with the gold-standard tests from their respective cognitive domains. The SCS total score and its memory scores all correlated positively with relative volumes of the whole hippocampus and almost all subregions, after controlling for age, sex, and education. The SCS has good diagnostic accuracy for detecting MCI and AD dementia and has the potential to facilitate large-scale screening in the general community.

Diffusion and structural MRI as potential biomarkers in people with Parkinson's disease and cognitive impairment.

To explore the neuroimage change in Parkinson's disease (PD) patients with cognitive impairments, this study investigated the correlation between plasma biomarkers and morphological brain changes in patients with normal cognition and mild cognitive impairment. The objective was to identify the potential target deposition regions of the plasma biomarkers and to search for the relevant early neuroimaging biomarkers on the basis of different cognitive domains. Structural brain MRI and diffusion weighted images were analyzed from 49 eligible PD participants (male/female: 27/22; mean age: 73.4 ± 8.5years) from a retrospective analysis. Plasma levels of α-synuclein, amyloid beta peptide, and total tau were collected. A comprehensive neuropsychological assessment of the general and specific cognitive domains was performed. Difference between PD patients with normal cognition and impairment was examined. Regression analysis was performed to evaluate the correlation between image-derived index and plasma biomarkers or neuropsychological assessments. Significant correlation was found between plasma Aβ-42 level and fractional anisotropy of the middle occipital, angular, and middle temporal gyri of the left brain, as well as plasma T-tau level and the surface area of the isthmus or the average thickness of the posterior part of right cingulate gyrus. Visuospatial and executive function is positively correlated with axial diffusivity in bilateral cingulate gyri. In nondemented PD patients, the target regions for plasma deposition might be located in the cingulate, middle occipital, angular, and middle temporal gyri. Changes from multiple brain regions can be correlated to the performance of different cognitive domains. Cognitive impairment in Parkinson's disease is primarily linked to biomarkers associated with Alzheimer's disease rather than those related to Parkinson's disease and resembles the frontal variant of Alzheimer's disease, which may guide management strategies for cognitive impairment in Parkinson's disease. • Fractional anisotropy, surface area, and thickness in the cingulate, middle occipital, angular, and middle temporal gyri can be significantly correlated with plasma Aβ-42 and T-tau level. • Axial diffusivity in the cingulate gyri was correlated with visuospatial and executive function. • The pattern of cognitive impairment in Parkinson's disease can be similar to the frontal variant than typical Alzheimer's disease.

TRAPPC9-Related Intellectual Disability: Report of Two New Cases and Review of the Literature

Introduction: Hereditary forms of intellectual disability (ID), an estimated prevalence ranging between 1% and 3% in the general population, are among the most important problems in health care. Especially, autosomal-recessive ID has a very heterogeneous molecular basis and a lack of specific phenotypic features. Methods: Here, we report on two unrelated patients with autosomal-recessive ID, microcephaly, and autistic features and review the patients with TRAPPC9-related ID. Whole-exome sequencing and array CGH were performed for molecular diagnosis of the patients. Results: The first case has a microdeletion on chromosome 8q24.23-q24.3 region which is 1.7 Mb in length and includes the last 5 exons of TRAPPC9, and c.3435delG [p.Thr1146Profs*8] deletion. The second case has a homozygous missense c.623A>C (p.His208Pro) variant in TRAPPC9 which is detected by means of whole-exome sequencing study of the proband. We also reviewed the clinical findings and mutation spectrum of all patients with TRAPPC9-related ID reported so far. Conclusions: Our study showed that the most consistent clinical findings for TRAPPC9-related ID are ID, microcephaly, and some structural brain MRI abnormalities. The mutations in the TRAPPC9 are scattered throughout all exons of TRAPPC9 indicating there is no hot spot mutation region in this gene.

Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance

BackgroundAutism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person’s perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure.MethodsWe selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter‐regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups.ResultsThe PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes.LimitationsThere is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results.ConclusionsASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.

Association of Choroid Plexus Volume With Serum Biomarkers, Clinical Features, and Disease Severity in Patients With Frontotemporal Lobar Degeneration Spectrum.

Choroid plexus (ChP) is emerging as a key brain structure in the pathophysiology of neurodegenerative disorders. In this observational study we investigated ChP volume in a large cohort of frontotemporal lobar degeneration (FTLD) spectrum patients to explore a possible link between ChP volume and other disease-specific biomarkers. Participants included patients meeting clinical criteria for a probable syndrome in the FTLD spectrum. Structural brain MRI imaging, serum neurofilament light (NfL) and serum phosphorylated-Tau181 (p-Tau181), cognitive and behavioral data were collected. MRI ChP volumes were obtained from an ad-hoc segmentation model based on a Gaussian Mixture Models (GMM) algorithm. Three-hundred and sixteen patients within FTLD spectrum were included in this study, specifically 135 patients diagnosed with behavioral variant frontotemporal dementia (bvFTD), 75 primary progressive aphasia (PPA), 46 progressive supranuclear palsy (PSP), 60 corticobasal syndrome (CBS). In addition, 82 age-matched healthy subjects were recruited as controls (HC). ChP volume was significantly larger in FTLD patients compared with HC, across the clinical subtype. Moreover, we found a significant difference in ChP volume between HC and patients stratified for disease-severity based on CDRplus NACC FTLD, including patients at very early stage of the disease. Interestingly, ChP volume correlated with serum NfL, cognitive/behavioural deficits, as well as with patterns of cortical atrophy. Finally, ChP volume seemed to discriminate HC from FTLD patients better than other previously identified brain structure volumes. Considering the clinical, pathological, and genetic heterogeneity of the disease, ChP could represent a potential biomarker across the FTLD spectrum, especially at the early stage of disease. Further longitudinal studies are needed to establish its role in disease onset and progression. This study provides Class III evidence that choroid plexus volume, as measured on MRI scan, can assist in differentiating patients with FTLD from healthy controls, and in characterizing disease severity.

Novel effects of Ras-MAPK pathogenic variants on the developing human brain and their link to gene expression and inhibition abilities

The RASopathies are genetic syndromes associated with pathogenic variants causing dysregulation of the Ras/mitogen-activated protein kinase (Ras-MAPK) pathway, essential for brain development, and increased risk for neurodevelopmental disorders. Yet, the effects of most pathogenic variants on the human brain are unknown. We examined: (1) How Ras-MAPK activating variants of PTPN11/SOS1 protein-coding genes affect brain anatomy. (2) The relationship between PTPN11 gene expression levels and brain anatomy, and (3) The relevance of subcortical anatomy to attention and memory skills affected in the RASopathies. We collected structural brain MRI and cognitive-behavioral data from 40 pre-pubertal children with Noonan syndrome (NS), caused by PTPN11 (n = 30) or SOS1 (n = 10) variants (age 8.53 ± 2.15, 25 females), and compared them to 40 age- and sex-matched typically developing controls (9.24 ± 1.62, 27 females). We identified widespread effects of NS on cortical and subcortical volumes and on determinants of cortical gray matter volume, surface area (SA), and cortical thickness (CT). In NS, we observed smaller volumes of bilateral striatum, precentral gyri, and primary visual area (d’s < −0.8), and extensive effects on SA (d’s > |0.8|) and CT (d’s > |0.5|) relative to controls. Further, SA effects were associated with increasing PTPN11 gene expression, most prominently in the temporal lobe. Lastly, PTPN11 variants disrupted normative relationships between the striatum and inhibition functioning. We provide evidence for the effects of Ras-MAPK pathogenic variants on striatal and cortical anatomy as well as links between PTPN11 gene expression and cortical SA increases, and striatal volume and inhibition skills. These findings provide essential translational information on the Ras-MAPK pathway’s effect on human brain development and function.

A Pilot Study of Short-Course Oral Vitamin A and Aerosolised Diffuser Olfactory Training for the Treatment of Smell Loss in Long COVID.

Background: Olfactory dysfunction (OD) is a common neurosensory manifestation in long COVID. An effective and safe treatment against COVID-19-related OD is needed. Methods: This pilot trial recruited long COVID patients with persistent OD. Participants were randomly assigned to receive short-course (14 days) oral vitamin A (VitA; 25,000 IU per day) and aerosolised diffuser olfactory training (OT) thrice daily (combination), OT alone (standard care), or observation (control) for 4 weeks. The primary outcome was differences in olfactory function by butanol threshold tests (BTT) between baseline and end-of-treatment. Secondary outcomes included smell identification tests (SIT), structural MRI brain, and serial seed-based functional connectivity (FC) analyses in the olfactory cortical network by resting-state functional MRI (rs-fMRI). Results: A total of 24 participants were randomly assigned to receive either combination treatment (n = 10), standard care (n = 9), or control (n = 5). Median OD duration was 157 days (IQR 127-175). Mean baseline BTT score was 2.3 (SD 1.1). At end-of-treatment, mean BTT scores were significantly higher for the combination group than control (p < 0.001, MD = 4.4, 95% CI 1.7 to 7.2) and standard care (p = 0.009) groups. Interval SIT scores increased significantly (p = 0.009) in the combination group. rs-fMRI showed significantly higher FC in the combination group when compared to other groups. At end-of-treatment, positive correlations were found in the increased FC at left inferior frontal gyrus and clinically significant improvements in measured BTT (r = 0.858, p < 0.001) and SIT (r = 0.548, p = 0.042) scores for the combination group. Conclusions: Short-course oral VitA and aerosolised diffuser OT was effective as a combination treatment for persistent OD in long COVID.