Structural Magnetic Resonance Imaging Scans Research Articles

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...

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.

Intra-individual cortical networks in Anorexia Nervosa: Evidence from a longitudinal dataset.

This work investigates cortical thickness (CT) and gyrification patterns in Anorexia Nervosa (AN) before and after short-term weight restoration using graph theory tools. 38 female adolescents with AN underwent structural magnetic resonance imaging scans at baseline and after - on average-3.5months following short-term weight restoration while 53 age-matched healthy controls (HCs) were scanned once. Graph measures were compared between groups and longitudinally within the AN group. Associations with clinical measures such as age of onset, duration of illness, BMI standard deviation score (BMI-SDS), and longitudinal weight changes were tested via stepwise regression. Cortical thickness graphs of patients with acute AN displayed lower modularity and small-world index (SWI) than HCs. Modularity recovered after weight gain. Reduced global efficiency and SWI were observed in patients at baseline compared to HCs based on gyrification networks. Significant associations between local clustering of CT at admission and BMI-SDS, and clustering/global efficiency of gyrification and duration of illness emerged. Our results indicate a shift towards less organised CT networks in patients with acute AN. After weight recovery, the disarrangement seems to be partially reduced. However, longer-term follow-ups are needed to determine whether cortical organizational patterns fully return to normal.

Volumetric magnetic resonance imaging differences between complex febrile seizure and recurrent simple febrile seizure

PurposeWe investigated the volumetric differences in cortical and subcortical structures between patients with complex febrile seizure (FS) and recurrent simple FS. We aimed to identify the brain morphological patterns of children with complex FS. MethodsTwenty-five patients with complex FS and age- and sex-matched 25 patients with recurrent simple FS with structural magnetic resonance imaging (MRI) scans were studied. Cortical volumetric analysis was performed using a voxel-based morphometry method with the CAT12 toolbox within SPM12. FSL-FIRST was used to obtain volume measures of subcortical deep grey matter structures (amygdala, caudate nucleus, thalamus, nucleus accumbens, putamen, globus pallidus, and hippocampus). The volumetric asymmetry index (AI) and laterality index (LI) were calculated for each subcortical structure. ResultsCompared with recurrent simple FS, complex FS demonstrated lower volume in the left putamen (p = .003) and right nucleus accumbens (p = .001). Additionally, patients with complex FS presented a higher magnitude of AI of the nucleus accumbens (p < .001) compared with recurrent simple FS. ConclusionsThe findings indicate that volumetric analysis may be a useful marker for the detection of FS-induced changes that reflect microstructural alterations. This study is the first to report on alterations in the putamen and nucleus accumbens in FS.

A neuroimaging study of altered cortical and subcortical volume in adolescent methamphetamine users

Purpose: The aim of this study was is to compare brain structure volume, including cortical and subcortical regions of adolescents- methamphetamine users versus non-users.&#x0D; Materials and Methods: The study was designed to be cross-sectional, and structural magnetic resonance imaging scans were obtained from the participants, including ten methamphetamine users and nine non-users. volBrain program was used to evaluate the images. &#x0D; Results: The results showed that methamphetamine users altered brain structures- temporal, parietal lobes, nucleus accumbens, amygdala, hippocampus, and thalamus volume. Also, the statistically significant difference in the volume between methamphetamine users and non-users was found in subcortical regions except putamen by age. Volumetric analysis of methamphetamine use in adolescents confirms a reduction in temporal lobes (methamphetamine users M±SD=3.43±0.18 non-users M±SD=3.48±0.22) and parietal lobes (methamphetamine users M±SD=2.23±0.24, non-users M±SD=2.37±0.33) in cortical regions in the brain as tissue volume. However, methamphetamine uses caused an increase in volume in the subcortical regions.&#x0D; Conclusion: Methamphetamine use appears to show decreased volume in the brain regions with age, which has adverse effects on cognitive, emotional, memory, and social abilities.

Associations between parental depression and anxiety symptom severity and their Offspring's cortical thickness and subcortical volume

Depression and anxiety are associated with grey matter changes in subcortical regions in adults and adolescents. Parent psychopathology is associated with offspring brain structure, but it's unclear whether altered brain structure in children is associated with severity of parental depression and anxiety symptoms. We examined 123 youth (Mean age = 13.64; 62% female) with no clinically significant history of depression or anxiety and one parent diagnosed with current or past depressive or anxiety disorders. Parents completed the Mini International Neuropsychiatric Interview to assess diagnostic status and the Beck Depression Inventory-II, and the Generalized Anxiety Disorder-7 to assess current symptom severity. Youth underwent T1 weighted structural Magnetic Resonance Imaging scans. Bivariate analyses revealed higher parental depressive severity was not significantly associated with offspring grey matter. Parental anxiety severity was significantly associated with less left global surface area. When controlling for offspring age, sex and intracranial volume (ICV), offspring right surface area was negatively associated with parental depressive severity at a trend level. In previously depressed parents, greater parental depressive severity was significantly associated with offspring decreased left and right surface area. There were no significant associations between parental anxiety severity in previously depressed parents and offspring subcortical or cortical brain regions. These results highlight associations between parental depressive symptom severity and offspring brain structure and suggest that even within an already high-risk group of adolescents, there may be altered cortical surface area depending on parent symptom severity. This may help identify youth most at risk for developing a mood disorder and could help further early intervention and identification efforts.

Alterations of brainstem volume in patients with first-episode and recurrent major depressive disorder

BackgroundMajor depressive disorder (MDD) is a prevalent mental health condition characterized by recurrent episodes in a substantial proportion of patients. The number of previous episodes is one of the most crucial predictors of depression recurrence. However, the underlying neural mechanisms remain unclear. To date, there have been limited neuroimaging studies investigating morphological changes of the brainstem in patients with first-episode MDD (FMDD) and recurrent MDD (RMDD). This study aimed to examine volumetric changes of individual brainstem regions in relation to the number of previous episodes and disease duration.MethodA total of 111 individuals including 36 FMDD, 25 RMDD, and 50 healthy controls (HCs) underwent T1-weighted structural magnetic resonance imaging scans. A Bayesian segmentation algorithm was used to analyze the volume of each brainstem region, including the medulla oblongata, pons, midbrain, and superior cerebellar peduncle (SCP), as well as the whole brainstem volume. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons. Partial correlation analyses were further conducted to identify associations between regional volumes and clinical features.ResultsThe ANOVA revealed significant brainstem volumetric differences among three groups in the pons, midbrain, SCP, and the whole brainstem (F = 3.996 ~ 5.886, adjusted p = 0.015 ~ 0.028). As compared with HCs, both groups of MDD patients showed decreased volumes in the pons as well as the entire brainstem (p = 0.002 ~ 0.034), however, only the FMDD group demonstrated a significantly reduced volume in the midbrain (p = 0.003). Specifically, the RMDD group exhibited significantly decreased SCP volume when comparing to both FMDD (p = 0.021) group and HCs (p = 0.008). Correlation analyses revealed that the SCP volumes were negatively associated with the number of depressive episodes (r=-0.36, p < 0.01) and illness duration (r=-0.28, p = 0.035) in patients with MDD.ConclusionThe present findings provided evidence of decreased brainstem volume involving in the pathophysiology of MDD, particularly, volumetric reduction in the SCP might represent a neurobiological marker for RMDD. Further research is needed to confirm our observations and deepen our understanding of the neural mechanisms underlying depression recurrence.

Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder

BackgroundSocial anxiety disorder (SAD) and major depressive disorder (MDD) are characterized by behavioral abnormalities in motivational systems, namely the behavioral inhibition system (BIS) and behavioral activation system (BAS). Limited studies indicate brain volume in regions that support emotion, learning/memory, reward, and cognitive functions relate to BIS/BAS. To increase understanding of BIS/BAS, the current study used a network approach. MethodsPatients with SAD (n = 59), patients with MDD (n = 64), and healthy control participants (n = 36) completed a BIS/BAS questionnaire and structural magnetic resonance imaging scans; volumetric regions of interest comprised cortical and limbic structures based on previous BIS/BAS studies. A Bayesian Gaussian graphical model was used for each diagnostic group, and groups were compared. Among network metrics, bridge centrality was of primary interest. Analysis of variance evaluated BIS/BAS behaviors between groups. ResultsBridge centrality showed hippocampus positively related to BAS, but not to BIS, in the MDD group; no findings were observed in the SAD or control groups. Yet, network density (i.e., overall strength of relationships between variables) and degree centrality (i.e., overall relationship between one variable to all other variables) showed that cortical (e.g., precuneus, medial orbitofrontal) and subcortical (e.g., amygdala, hippocampus) regions differed between diagnostic groups. Analysis of variance results showed BAS was lower in the MDD/SAD groups compared with the control group, while BIS was higher in the SAD group relative to the MDD group, which in turn was higher than the control group. ConclusionsPreliminary findings indicate that network-level aberrations may underlie motivational abnormalities in MDD and SAD. Evidence of BIS/BAS differences builds on previous work that points to shared and distinct motivational differences in internalizing psychopathologies.

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. For this reason, several machine learning-based methods have been developed for the automatic quality control of structural MRI scans. Deep learning offers a promising solution to this problem, however, given its data-hungry nature and the scarcity of expert-annotated datasets, its advantage over traditional machine learning methods in identifying motion-corrupted brain scans is yet to be determined. In the present study, we investigated the relative advantage of the two methods in structural MRI quality control. To this end, we collected publicly available T1-weighted images and scanned subjects in our own lab under conventional and active head motion conditions. The quality of the images was rated by a team of radiologists from the point of view of clinical diagnostic use. We present a relatively simple, lightweight 3D convolutional neural network trained in an end-to-end manner that achieved a test set (N=411) balanced accuracy of 94.41% in classifying brain scans into clinically usable or unusable categories. A support vector machine trained on image quality metrics achieved a balanced accuracy of 88.44% on the same test set. Statistical comparison of the two models yielded no significant difference in terms of confusion matrices, error rates, or receiver operating characteristic curves. Our results suggest that these machine learning methods are similarly effective in identifying severe motion artifacts in brain MRI scans, and underline the efficacy of end-to-end deep learning-based systems in brain MRI quality control, allowing the rapid evaluation of diagnostic utility without the need for elaborate image pre-processing.

Pre-coronavirus disease 2019 brain structure might be associated with social anxiety alterations during the pandemic.

Pre-coronavirus disease 2019 brain structure might be associated with social anxiety alterations during the pandemic.

Neurocognitive basis of model-based decision making and its metacontrol in childhood

Human behavior is supported by both goal-directed (model-based) and habitual (model-free) decision-making, each differing in its flexibility, accuracy, and computational cost. The arbitration between habitual and goal-directed systems is thought to be regulated by a process known as metacontrol. However, how these systems emerge and develop remains poorly understood. Recently, we found that while children between 5 and 11 years displayed robust signatures of model-based decision-making, which increased during this developmental period, there were substantial individual differences in the display of metacontrol. Here, we inspect the neurocognitive basis of model-based decision-making and metacontrol in childhood and focus this investigation on executive functions, fluid reasoning, and brain structure. A total of 69 participants between the ages of 6–13 completed a two-step decision-making task and an extensive behavioral test battery. A subset of 44 participants also completed a structural magnetic resonance imaging scan. We find that individual differences in metacontrol are specifically associated with performance on an inhibition task and individual differences in thickness of dorsolateral prefrontal, temporal, and superior-parietal cortices. These brain regions likely reflect the involvement of cognitive processes crucial to metacontrol, such as cognitive control and contextual processing.

Early Gray Matter Structural Covariance Predicts Longitudinal Gain in Arithmetic Ability in Children

Previous neuroimaging studies on arithmetic development have mainly focused on functional activation or functional connectivity between brain regions. It remains largely unknown how brain structures support arithmetic development. The present study investigated whether early gray matter structural covariance contributes to later gain in arithmetic ability in children. We used a public longitudinal sample comprising 63 typically developing children. The participants received structural magnetic resonance imaging scanning when they were 11 years old and were tested with a multiplication task at 11 years old (time 1) and 13 years old (time 2), respectively. Mean gray matter volumes were extracted from eight brain regions of interest to anchor salience network (SN), frontal-parietal network (FPN), motor network (MN), and default mode network (DMN) at time 1. We found that longitudinal gain in arithmetic ability was associated with stronger structural covariance of the SN seed with frontal and parietal regions and stronger structural covariance of the FPN seed with insula, but weaker structural covariance of the FPN seed with motor and temporal regions, weaker structural covariance of the MN seed with frontal and motor regions, and weaker structural covariance of the DMN seed with temporal region. However, we did not detect correlation between longitudinal gain in arithmetic ability and behavioral measure or regional gray matter volume at time 1. Our study provides novel evidence for a specific contribution of gray matter structural covariance to longitudinal gain in arithmetic ability in childhood.

Early structural brain markers for Alzheimer’s Disease in women: Brain‐behaviour relationships after ovarian removal

AbstractBackgroundWomen with bilateral salpingo‐oophorectomy (BSO; removal of ovaries and fallopian tubes) prior to age 50 have increased Alzheimer’s disease (AD) risk (Rocca et al., 2007), but the neural mechanisms for this are unclear. Abilities involving manipulation/maintenance aspects of working memory (WM) decline post‐BSO, and this effect may be reduced by estradiol‐based hormone therapy (ET; Gervais et al., 2020). Considering decline in manipulation/maintenance aspects of WM in AD (Germano &amp; Kinsella, 2005), we aimed to understand the relationships between brain structure and WM after BSO.MethodWomen with BSO taking ET (BSO+ET: M±SDage = 44.92±5.11, n = 26) or not taking ET (BSO: M±SDage = 45.62±5.04, n = 26) were compared to age‐matched premenopausal controls (AMC: M±SDage = 43.71±2.92, n = 42) recruited from Toronto, Montreal, and Linköping. T1‐weighted structural magnetic resonance imaging scans were acquired. Voxel‐based morphometry measures of gray matter volume were obtained (CIVET pipeline, Ad‐Dab’bagh et al., 2005). Digit Span Backward (DSB) and Forward (DSF) subtests (Wechsler, 1945) were administered to assess manipulation/maintenance and updating/maintenance aspects of WM, respectively. Multivariate Behaviour Partial Least Squares (McIntosh &amp; Lobaugh, 2004) was used to compare the correlation between performance (DSB and DSF) and volume between groups.ResultThere were no group differences in DSB/DSF total span performance. AMC did not show a significant relationship between volume and WM (Figure1A). For BSO and BSO+ET, gray matter volume in key WM regions, including bilateral supplementary motor area, inferior temporal gyrus, superior and inferior frontal gyri, left precuneus, and right cuneus, was negatively associated with DSB performance (Figure1B warm colours), while hippocampal volume was positively associated with DSB performance (Figure1B cold colours). These relationships were strong for BSO (R = ‐0.78), while BSO+ET (R = ‐0.37; moderate correlation) showed an intermediate phenotype between BSO and AMC (R = 0.14; small correlation).ConclusionManipulation/maintenance aspects of WM and regional volume relationships depended on menopause status and ET. Without ET after BSO, successful WM performance correlated more strongly with hippocampal volume, which is usually the case for high memory load WM tasks (Geva et al., 2016). Even when memory changes are undetected by standard neuropsychological tests, brain‐behaviour relationships may prove a useful tool for assessing early changes in individuals at increased AD risk.

A randomized controlled trial examining the effects of behavioral weight loss treatment on hippocampal volume and neurocognition

Background/purposeObesity in midlife is an established risk factor for dementia. In middle-aged adults, elevated body mass index (BMI) is associated with lower neurocognition and smaller hippocampal volumes. It is unclear whether behavioral weight loss (BWL) can improve neurocognition. The purpose of this study was to evaluate whether BWL, compared to wait list control (WLC), improved hippocampal volume and neurocognition. We also examined if baseline hippocampal volume and neurocognition were associated with weight loss. MethodsWe randomly assigned women with obesity (N = 61; mean±SD age=41.1 ± 9.9 years; BMI=38.6 ± 6.2 kg/m2; and 50.8% Black) to BWL or WLC. Participants completed assessments at baseline and follow-up including T1-weighted structural magnetic resonance imaging scans and the National Institutes of Health (NIH) Toolbox Cognition Battery. ResultsThe BWL group lost 4.7 ± 4.9% of initial body weight at 16–25 weeks, which was significantly more than the WLC group which gained 0.2 ± 3.5% (p < 0.001). The BWL and WLC groups did not differ significantly in changes in hippocampal volume or neurocognition (ps>0.05). Baseline hippocampal volume and neurocognition scores were not significantly associated with weight loss (ps>0.05). Conclusions and implicationsContrary to our hypothesis, we found no overall benefit of BWL relative to WLC on hippocampal volumes or cognition in young- and middle-aged women. Baseline hippocampal volume and neurocognition were not associated with weight loss.

Deep whole brain segmentation of 7T structural MRI.

7T magnetic resonance imaging (MRI) has the potential to drive our understanding of human brain function through new contrast and enhanced resolution. Whole brain segmentation is a key neuroimaging technique that allows for region-by-region analysis of the brain. Segmentation is also an important preliminary step that provides spatial and volumetric information for running other neuroimaging pipelines. Spatially localized atlas network tiles (SLANT) is a popular 3D convolutional neural network (CNN) tool that breaks the whole brain segmentation task into localized sub-tasks. Each sub-task involves a specific spatial location handled by an independent 3D convolutional network to provide high resolution whole brain segmentation results. SLANT has been widely used to generate whole brain segmentations from structural scans acquired on 3T MRI. However, the use of SLANT for whole brain segmentation from structural 7T MRI scans has not been successful due to the inhomogeneous image contrast usually seen across the brain in 7T MRI. For instance, we demonstrate the mean percent difference of SLANT label volumes between a 3T scan-rescan is approximately 1.73%, whereas its 3T-7T scan-rescan counterpart has higher differences around 15.13%. Our approach to address this problem is to register the whole brain segmentation performed on 3T MRI to 7T MRI and use this information to finetune SLANT for structural 7T MRI. With the finetuned SLANT pipeline, we observe a lower mean relative difference in the label volumes of ~8.43% acquired from structural 7T MRI data. Dice similarity coefficient between SLANT segmentation on the 3T MRI scan and the after finetuning SLANT segmentation on the 7T MRI increased from 0.79 to 0.83 with p<0.01. These results suggest finetuning of SLANT is a viable solution for improving whole brain segmentation on high resolution 7T structural imaging.

Sleep across the first year of life is prospectively associated with brain volume in 12-months old infants

ObjectiveLonger sleep duration in infancy supports cognitive and affective functioning – likely through effects on brain development. From childhood through old age, there is evidence for a close link between sleep and brain volume. However, little is known about the association between sleep duration and brain volume in infancy, a developmental period of unprecedented brain maturation. This study aimed to close this gap by assessing sleep duration across the first year of life and gray and white matter volume at 12-mo age. MethodInfant sleep duration trajectories across the first year of life were based on maternal reports at 1, 3, 6, 9, and 12 months of age. Infant specific trajectories were generated by running a logarithmic regression for each infant and residualizing the resulting slopes for their intercept. Structural magnetic resonance imaging (MRI) scans were acquired at 12-mo age. Gray and white matter volume estimates were residualized for intracranial volume and age at scan. ResultsData to calculate sleep trajectories was available for 112 infants. Overall, sleep duration decreased over the course of the first year of life and was best described by a logarithmic function. Of these infants, data on brain volume was available for 45 infants at 12-mo age. Infants whose sleep duration decreased less during the first year of life relative to their intercept had, on average, greater white matter volume (β = .36, p = .02). Furthermore, average sleep duration across the first year of life, and sleep duration specifically at 6 and 9 months were positively associated with white matter volume. Sleep duration during the first year of life was not significantly associated with gray matter volume at 12-mo age. ConclusionSufficient sleep duration may benefit infant white matter development – possibly by supporting myelination. The fact that sleep duration was not associated with gray matter volume is in line with preclinical studies suggesting that sleep may be crucial for the balance between synaptogenesis and synaptic pruning but not necessarily relate to a net increase in gray matter volume. Supporting sleep during periods of rapid brain development and intervening in case of sleep problems may have long-term benefits for cognitive function and mental health.

An unsupervised learning approach to diagnosing Alzheimer’s disease using brain magnetic resonance imaging scans

BackgroundAlzheimer’s disease (AD) is the most common cause of dementia, characterised by behavioural and cognitive impairment. Due to the lack of effectiveness of manual diagnosis by doctors, machine learning is now being applied to diagnose AD in many recent studies. Most research developing machine learning algorithms to diagnose AD use supervised learning to classify magnetic resonance imaging (MRI) scans. However, supervised learning requires a considerable volume of labelled data and MRI scans are difficult to label. ObjectiveThis study applied a statistical method and unsupervised learning methods to discriminate between scans from cognitively normal (CN) and people with AD using a limited number of labelled structural MRI scans. MethodsWe used two-sample t-tests to detect the AD-relevant regions, and then employed an unsupervised learning neural network to extract features from the regions. Finally, a clustering algorithm was implemented to discriminate between CN and AD data based on the extracted features. The approach was tested on baseline brain structural MRI scans from 429 individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), of which 231 were CN and 198 had AD. ResultsThe abnormal regions around the lower parts of limbic system were indicated as AD-relevant regions based on the two-sample t-test (p < 0.001), and the proposed method yielded an accuracy of 0.84 for discriminating between CN and AD. ConclusionThe study combined statistical and unsupervised learning methods to identify scans of people with AD. This method can detect AD-relevant regions and could be used to accurately diagnose AD; it does not require large amounts of labelled MRI scans. Our research could help in the automatic diagnosis of AD and provide a basis for diagnosing stable mild cognitive impairment (stable MCI) and progressive mild cognitive impairment (progressive MCI).

Brain volumetrics across the lifespan of the rhesus macaque

The rhesus macaque is a long-lived nonhuman primate (NHP) with a brain structure similar to humans, which may represent a valuable translational animal model in which to study human brain aging. Previous magnetic resonance imaging (MRI) studies of age in rhesus macaque brains have been prone to low statistical power, unbalanced sex ratio and lack of a complete age range. To overcome these problems, the current study surveyed structural T1-weighted magnetic resonance imaging scans of 66 animals, 34 females (aged 6-31 years) and 32 males (aged 5-27 years). Differences observed in older animals, included enlargement of the lateral ventricles and a smaller volume in the frontal cortex, caudate, putamen, hypothalamus, and thalamus. Unexpected, greater volume, were measured in older animals in the hippocampus, amygdala, and globus pallidus. There were also numerous differences between males and females with respect to age in both white and gray matter regions. As an apparent model of normative human aging, the macaque is ideal for studying induction and mitigation of neurodegenerative disease.

Role of Enlarged Perivascular Space in the Temporal Lobe in Cerebral Amyloidosis.

Although growing evidence suggests that perivascular space (PVS) serves as a clearance route for amyloid and tau, the association between enlarged PVS (EPVS) and Alzheimer disease is highly inconsistent across studies. As the conventional visual rating systems for EPVS were insufficient to predict amyloid/tau/neurodegeneration (A/T/N) status, we developed a new rating scale for EPVS located in the temporal lobe (T-EPVS). EPVS located in the basal ganglia (BG-EPVS), centrum semiovale (CS-EPVS), and T-EPVS was visually rated in 272 individuals (healthy controls, n=96; mild cognitive impairment, n=106; dementia, n=70) who underwent structural magnetic resonance imaging (MRI) and dual positron emission tomography scans (18 F-flortaucipir and 18 F-florbetaben). T-EPVS and BG-EPVS were defined as high degree when the counts in any hemisphere were >10, and the CS-EPVS cutoff was >20. Logistic regression models were constructed to investigate whether the regional EPVS burden was predictive of A/T/N status. The derived models were externally validated in a temporal validation cohort (n=195) that underwent MRI studies using a different scanner. Compared with those with low-degree T-EPVS (23/136, 16.9%), individuals with high-degree T-EPVS/CS-EPVS but low-degree BG-EPVS were more likely to exhibit amyloid positivity (46/56, 82.1%). High-degree T-EPVS burden (odds ratio [OR]=7.251, 95% confidence interval [CI]=3.296-15.952) and low-degree BG-EPVS (OR=0.241, 95% CI=0.109-0.530) were predictive of amyloid positivity. Although high-degree T-EPVS was associated with tau positivity, the association was no longer significant after adjusting for amyloid and neurodegeneration status. Investigating the burden and topographic distribution of EPVS including T-EPVS may be useful for predicting amyloid status, indicating that impaired perivascular drainage may contribute to cerebral amyloidosis. ANN NEUROL 2023;93:965-978.

The association between genetic variations and morphology-based brain networks changes in Alzheimer's disease.

Alzheimer's disease (AD) is a highly heritable disease. The morphological changes of cortical cortex (such as, cortical thickness and surface area) in AD always accompany by the change of the functional connectivity to other brain regions and influence the short- and long-range brain network connections, causing functional deficits of AD. In this study, the first hypothesis is that genetic variations might affect morphology-based brain networks, leading to functional deficits; the second hypothesis is that protein-protein interaction (PPI) between the candidate proteins and known interacting proteins to AD might exist and influence AD. 600 470 variants and structural magnetic resonance imaging scans from 175 AD patients and 214 healthy controls were obtained from the Alzheimer's Disease Neuroimaging Initiative-1 database. A co-sparse reduced-rank regression model was fit to study the relationship between non-synonymous mutations and morphology-based brain networks. After that, PPIs between selected genes and BACE1, an enzyme that was known to be related to AD, are explored by using molecular dynamics (MD) simulation and co-immunoprecipitation (Co-IP) experiments. Eight genes affecting morphology-based brain networks were identified. The results of MD simulation showed that the PPI between TGM4 and BACE1 was the strongest among them and its interaction was verified by Co-IP. Hence, gene variations influence morphology-based brain networks in AD, leading to functional deficits. This finding, validated by MD simulation and Co-IP, suggests that the effect is robust.