Opposite changes in morphometric similarity of medial reward and lateral non-reward orbitofrontal cortex circuits in obesity.

Obesity has a profound impact on metabolic health thereby adversely affecting brain structure and function. However, the majority of previous studies used a single structural index to investigate the link between brain structure and body mass index (BMI), which hinders our understanding of structural covariance between regions in obesity. This study aimed to examine the relationship between macroscale cortical organization and BMI using novel morphometric similarity networks (MSNs). The individual MSNs were first constructed from individual eight multimodal cortical morphometric features between brain regions. Then the relationship between BMI and MSNs within the discovery sample of 434 participants was assessed. The key findings were further validated in an independent sample of 192 participants. We observed that the lateral non-reward orbitofrontal cortex (lOFC) exhibited decoupling (i.e., reduction in integration) in obesity, which was mainly manifested by its decoupling with the cognitive systems (i.e., DMN and FPN) while the medial reward orbitofrontal cortex (mOFC) showed de-differentiation (i.e., decrease in distinctiveness) in obesity, which was mainly represented by its de-differentiation with the cognitive and attention systems (i.e., DMN and VAN). Additionally, the lOFC showed de-differentiation with the visual system in obesity, while the mOFC showed decoupling with the visual system and hyper-coupling with the sensory-motor system in obesity. As an important first step in revealing the role of underlying structural covariance in body mass variability, the present study presents a novel mechanism that underlies the reward-control interaction imbalance in obesity, thus can inform future weight-management approaches.

Functionally and structurally distinct fusiform face area(s) in over 1000 participants.

The fusiform face area (FFA) is a widely studied region causally involved in face perception. Even though cognitive neuroscientists have been studying the FFA for over two decades, answers to foundational questions regarding the function, architecture, and connectivity of the FFA from a large (N>1000) group of participants are still lacking. To fill this gap in knowledge, we quantified these multimodal features of fusiform face-selective regions in 1053 participants in the Human Connectome Project. After manually defining over 4,000 fusiform face-selective regions, we report five main findings. First, 68.76% of hemispheres have two cortically separate regions (pFus-faces/FFA-1 and mFus-faces/FFA-2). Second, in 26.69% of hemispheres, pFus-faces/FFA-1 and mFus-faces/FFA-2 are spatially contiguous, yet are distinct based on functional, architectural, and connectivity metrics. Third, pFus-faces/FFA-1 is more face-selective than mFus-faces/FFA-2, and the two regions have distinct functional connectivity fingerprints. Fourth, pFus-faces/FFA-1 is cortically thinner and more heavily myelinated than mFus-faces/FFA-2. Fifth, face-selective patterns and functional connectivity fingerprints of each region are more similar in monozygotic than dizygotic twins and more so than architectural gradients. As we share our areal definitions with the field, future studies can explore how structural and functional features of these regions will inform theories regarding how visual categories are represented in the brain.

Neuroimaging-based classification of PTSD using data-driven computational approaches: A multisite big data study from the ENIGMA-PGC PTSD consortium.

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group. METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality. RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60 % test AUC for s-MRI, 59 % for rs-fMRI and 56 % for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75 % AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance. CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.

PhiPipe: A multi-modal MRI data processing pipeline with test-retest reliability and predicative validity assessments.

Magnetic resonance imaging (MRI) has been one of the primary instruments to measure the properties of the human brain non-invasively in vivo. MRI data generally needs to go through a series of processing steps (i.e., a pipeline) before statistical analysis. Currently, the processing pipelines for multi-modal MRI data are still rare, in contrast to single-modal pipelines. Furthermore, the reliability and validity of the output of the pipelines are critical for the MRI studies. However, the reliability and validity measures are not available or adequate for almost all pipelines. Here, we present PhiPipe, a multi-modal MRI processing pipeline. PhiPipe could process T1-weighted, resting-state BOLD, and diffusion-weighted MRI data and generate commonly used brain features in neuroimaging. We evaluated the test-retest reliability of PhiPipe's brain features by computing intra-class correlations (ICC) in four public datasets with repeated scans. We further evaluated the predictive validity by computing the correlation of brain features with chronological age in three public adult lifespan datasets. The multivariate reliability and predictive validity of the PhiPipe results were also evaluated. The results of PhiPipe were consistent with previous studies, showing comparable or better reliability and validity when compared with two popular single-modality pipelines, namely DPARSF and PANDA. The publicly available PhiPipe provides a simple-to-use solution to multi-modal MRI data processing. The accompanied reliability and validity assessments could help researchers make informed choices in experimental design and statistical analysis. Furthermore, this study provides a framework for evaluating the reliability and validity of image processing pipelines.

Comparison and optimization of pCASL and VSASL for rat thoracolumbar spinal cord MRI at 9.4 T.

PURPOSE: To evaluate pseudo-continuous arterial spin labeling (pCASL) and velocity-selective arterial spin labeling (VSASL) for quantification of spinal cord blood flow (SCBF) in the rat thoracolumbar spinal cord. METHODS: Labeling efficiency (LE) was compared between pCASL and three VSASL variants in simulations and both phantom and in vivo experiments at 9.4 T. For pCASL, the effects of label plane position and shimming were systematically evaluated. For VSASL, the effects of composite pulses and phase cycling were evaluated to reduce artifacts. Additionally, vessel suppression, respiratory, and cardiac gating were evaluated to reduce motion artifacts. pCASL and VSASL maps of spinal cord blood flow were acquired with the optimized protocols. RESULTS: LE of the descending aorta was larger in pCASL compared to VSASL variants. In pCASL, LE off-isocenter was improved by local shimming positioned at the label plane and the anatomical level of labeling for the thoracic cord was only viable at the level of the T10 vertebra. Cardiac gating was essential to reduce motion artifacts. Both pCASL and VSASL successfully demonstrated comparable SCBF values in the thoracolumbar cord. CONCLUSION: pCASL demonstrated high and consistent LE in the thoracic aorta, and VSASL was also feasible, but with reduced efficiency. A combination of cardiac gating and recording of actual post-label delays was important for accurate SCBF quantification. These results highlight the challenges and solutions to achieve sufficient ASL labeling and contrast at high field in organs prone to motion.

Symptom-guided multimodal neuroimage fusion patterns in children with attention-deficit/hyperactivity disorder and its potential "brain structure-function-cognition-behavior" pathological pathways.

The "brain-cognition-behavior" process is an important pathological pathway in children with attention-deficit/hyperactivity disorder (ADHD). Symptom guided multimodal neuroimaging fusion can capture behaviorally relevant and intrinsically linked structural and functional features, which can help to construct a systematic model of the pathology. Analyzing the multimodal neuroimage fusion pattern and exploring how these brain features affect executive function (EF) and leads to behavioral impairment is the focus of this study. Based on gray matter volume (GMV) and fractional amplitude of low frequency fluctuation (fALFF) for 152 ADHD and 102 healthy controls (HC), the total symptom score (TO) was set as a reference to identify co-varying components. Based on the correlation between the identified co-varying components and EF, further mediation analysis was used to explore the relationship between brain image features, EF and clinical symptoms. This study found that the abnormalities of GMV and fALFF in ADHD are mainly located in the default mode network (DMN) and prefrontal-striatal-cerebellar circuits, respectively. GMV in ADHD influences the TO through Metacognition Index, while fALFF in HC mediates the TO through behavior regulation index (BRI). Further analysis revealed that GMV in HC influences fALFF, which further modulates BRI and subsequently affects hyperactivity-impulsivity score. To conclude, structural brain abnormalities in the DMN in ADHD may affect local brain function in the prefrontal-striatal-cerebellar circuit, making it difficult to regulate EF in terms of inhibit, shift, and emotional control, and ultimately leading to hyperactive-impulsive behavior.

Factors Requiring Improvement for Timely and Effective Treatment of Acute Stroke.

How to cite this article: Finsterer J. Factors Requiring Improvement for Timely and Effective Treatment of Acute Stroke. Indian J Crit Care Med 2023;27(12):947-948.

Multimodal Imaging Brain Markers in Early Adolescence Are Linked with a Physically Active Lifestyle.

The World Health Organization promotes physical exercise and a healthy lifestyle as means to improve youth development. However, relationships between physical lifestyle and human brain development are not fully understood. Here, we asked whether a human brain-physical latent mode of covariation underpins the relationship between physical activity, fitness, and physical health measures with multimodal neuroimaging markers. In 50 12-year old school pupils (26 females), we acquired multimodal whole-brain MRI, characterizing brain structure, microstructure, function, myelin content, and blood perfusion. We also acquired physical variables measuring objective fitness levels, 7 d physical activity, body mass index, heart rate, and blood pressure. Using canonical correlation analysis, we unravel a latent mode of brain-physical covariation, independent of demographics, school, or socioeconomic status. We show that MRI metrics with greater involvement in this mode also showed spatially extended patterns across the brain. Specifically, global patterns of greater gray matter perfusion, volume, cortical surface area, greater white matter extra-neurite density, and resting state networks activity covaried positively with measures reflecting a physically active phenotype (high fit, low sedentary individuals). Showing that a physically active lifestyle is linked with systems-level brain MRI metrics, these results suggest widespread associations relating to several biological processes. These results support the notion of close brain-body relationships and underline the importance of investigating modifiable lifestyle factors not only for physical health but also for brain health early in adolescence.SIGNIFICANCE STATEMENT An active lifestyle is key for healthy development. In this work, we answer the following question: How do brain neuroimaging markers relate with young adolescents' level of physical activity, fitness, and physical health? Combining advanced whole-brain multimodal MRI metrics with computational approaches, we show a robust relationship between physically active lifestyles and spatially extended, multimodal brain imaging-derived phenotypes. Suggesting a wider effect on brain neuroimaging metrics than previously thought, this work underlies the importance of studying physical lifestyle, as well as other brain-body relationships in an effort to foster brain health at this crucial stage in development.

An automated pipeline for extracting histological stain area fraction for voxelwise quantitative MRI-histology comparisons.

The acquisition of MRI and histology in the same post-mortem tissue sample enables direct correlation between MRI and histologically-derived parameters. However, there still lacks a standardised automated pipeline to process histology data, with most studies relying on manual intervention. Here, we introduce an automated pipeline to extract a quantitative histological measure for staining density (stain area fraction, SAF) from multiple immunohistochemical (IHC) stains. The pipeline is designed to directly address key IHC artefacts related to tissue staining and slide digitisation. Here, the pipeline was applied to post-mortem human brain data from multiple subjects, relating MRI parameters (FA, MD, RD, AD, R2*, R1) to IHC slides stained for myelin, neurofilaments, microglia and activated microglia. Utilising high-quality MRI-histology co-registrations, we then performed whole-slide voxelwise comparisons (simple correlations, partial correlations and multiple regression analyses) between multimodal MRI- and IHC-derived parameters. The pipeline was found to be reproducible, robust to artefacts and generalisable across multiple IHC stains. Our partial correlation results suggest that some simple MRI-SAF correlations should be interpreted with caution, due to the co-localisation of other tissue features (e.g., myelin and neurofilaments). Further, we find activated microglia-a generic biomarker of inflammation-to consistently be the strongest predictor of high DTI FA and low RD, which may suggest sensitivity of diffusion MRI to aspects of neuroinflammation related to microglial activation, even after accounting for other microstructural changes (demyelination, axonal loss and general microglia infiltration). Together, these results show the utility of this approach in carefully curating IHC data and performing multimodal analyses to better understand microstructural relationships with MRI.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

Outlier detection in multimodal MRI identifies rare individual phenotypes among more than 15,000 brains.

Outliers in neuroimaging represent spurious data or the data of unusual phenotypes that deserve special attention such as clinical follow-up. Outliers have usually been detected in a supervised or semi-supervised manner for labeled neuroimaging cohorts. There has been much less work using unsupervised outlier detection on large unlabeled cohorts like the UK Biobank brain imaging dataset. Given its large sample size, rare imaging phenotypes within this unique cohort are of interest, as they are often clinically relevant and could be informative for discovering new processes. Here, we developed a two-level outlier detection and screening methodology to characterize individual outliers from the multimodal MRI dataset of more than 15,000 UK Biobank subjects. In primary screening, using brain ventricles, white matter, cortical thickness, and functional connectivity-based imaging phenotypes, every subject was parameterized with an outlier score per imaging phenotype. Outlier scores of these imaging phenotypes had good-to-excellent test-retest reliability, with the exception of resting-state functional connectivity (RSFC). Due to the low reliability of RSFC outlier scores, RSFC outliers were excluded from further individual-level outlier screening. In secondary screening, the extreme outliers (1,026 subjects) were examined individually, and those arising from data collection/processing errors were eliminated. A representative subgroup of 120 subjects from the remaining non-artifactual outliers were radiologically reviewed, and radiological findings were identified in 97.5% of them. This study establishes an unsupervised framework for investigating rare individual imaging phenotypes within a large neuroimaging cohort.

Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors.

Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of 'opportunistic stacking' allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%-20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%-20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework.

Multimodal MRI examination of structural and functional brain changes in older women with breast cancer in the first year of antiestrogen hormonal therapy.

PURPOSE: Cancer patients are concerned about treatment-related cognitive problems. We examined effects of antiestrogen hormonal therapy on brain imaging metrics in older women with breast cancer. METHODS: Women aged 60 + treated with hormonal therapy only and matched non-cancer controls (n = 29/group) completed MRI and objective and self-reported cognitive assessment at pre-treatment/enrollment and 12 months later. Gray matter was examined using voxel-based morphometry (VBM), FreeSurfer, and brain age calculations. Functional MRI (fMRI) assessed working memory-related activation. Analyses examined cross-sectional and longitudinal differences and tested associations between brain metrics, cognition, and days on hormonal therapy. RESULTS: The cancer group showed regional reductions over 12 months in frontal, temporal, and parietal gray matter on VBM, reduced FreeSurfer cortical thickness in prefrontal, parietal, and insular regions, and increased working memory-related fMRI activation in frontal, cingulate, and visual association cortex. Controls showed only reductions in fusiform gyrus on VBM and FreeSurfer temporal and parietal cortex thickness. Women with breast cancer showed higher estimated brain age and lower regional gray matter volume than controls at both time points. The cancer group showed a trend toward lower performance in attention, processing speed, and executive function at follow-up. There were no significant associations between brain imaging metrics and cognition or days on hormonal therapy. CONCLUSION: Older women with breast cancer showed brain changes in the first year of hormonal therapy. Increased brain activation during working memory processing may be a sign of functional compensation for treatment-related structural changes. This hypothesis should be tested in larger samples over longer time periods. GOV IDENTIFIER: NCT03451383.

Altered cerebellar-motor loop in benign adult familial myoclonic epilepsy type 1: The structural basis of cortical tremor.

OBJECTIVE: Cortical tremor/myoclonus is the hallmark feature of benign adult familial myoclonic epilepsy (BAFME), the mechanism of which remains elusive. A hypothesis is that a defective control in the preexisting cerebellar-motor loop drives cortical tremor. Meanwhile, the basal ganglia system might also participate in BAFME. This study aimed to discover the structural basis of cortical tremor/myoclonus in BAFME. METHODS: Nineteen patients with BAFME type 1 (BAFME1) and 30 matched healthy controls underwent T1-weighted and diffusion tensor imaging scans. FreeSurfer and spatially unbiased infratentorial template (SUIT) toolboxes were utilized to assess the motor cortex and the cerebellum. Probabilistic tractography was generated for two fibers to test the hypothesis: the dentato-thalamo-(M1) (primary motor cortex) and globus pallidus internus (GPi)-thalamic projections. Average fractional anisotropy (FA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) of each tract were extracted. RESULTS: Cerebellar atrophy and dentate nucleus alteration were observed in the patients. In addition, patients with BAFME1 exhibited reduced AD and FA in the left and right dentato-thalamo-M1 nondecussating fibers, respectively false discovery rate (FDR) correction q < .05. Cerebellar projections showed negative correlations with somatosensory-evoked potential P25-N33 amplitude and were independent of disease duration and medication. BAFME1 patients also had increased FA and decreased MD in the left GPi-thalamic projection. Higher FA and lower RD in the right GPi-thalamic projection were also observed (FDR q < .05). SIGNIFICANCE: The present findings support the hypothesis that the cerebello-thalamo-M1 loop might be the structural basis of cortical tremor in BAFME1. The basal ganglia system also participates in BAFME1 and probably serves a regulatory role.

Multimodal Image Analysis of Apparent Brain Age Identifies Physical Fitness as Predictor of Brain Maintenance.

Maintaining a youthful brain structure and function throughout life may be the single most important determinant of successful cognitive aging. In this study, we addressed heterogeneity in brain aging by making image-based brain age predictions and relating the brain age prediction gap (BAPG) to cognitive change in aging. Structural, functional, and diffusion MRI scans from 351 participants were used to train and evaluate 5 single-modal and 4 multimodal prediction models, based on 7 regression methods. The models were compared on mean absolute error and whether they were related to physical fitness and cognitive ability, measured both currently and longitudinally, as well as study attrition and years of education. Multimodal prediction models performed at a similar level as single-modal models, and the choice of regression method did not significantly affect the results. Correlation with the BAPG was found for current physical fitness, current cognitive ability, and study attrition. Correlations were also found for retrospective physical fitness, measured 10 years prior to imaging, and slope for cognitive ability during a period of 15 years. The results suggest that maintaining a high physical fitness throughout life contributes to brain maintenance and preserved cognitive ability.

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks.

BACKGROUND: There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma. METHODS: This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both "functional data" from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and "structural data" (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants. RESULTS: The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94-0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes. CONCLUSIONS: A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Number http://ClinicalTrials.gov (No. NCT03482115).

Brain plasticity dynamics during tactile Braille learning in sighted subjects: Multi-contrast MRI approach.

A growing body of empirical evidence supports the notion of diverse neurobiological processes underlying learning-induced plasticity changes in the human brain. There are still open questions about how brain plasticity depends on cognitive task complexity, how it supports interactions between brain systems and with what temporal and spatial trajectory. We investigated brain and behavioural changes in sighted adults during 8-months training of tactile Braille reading whilst monitoring brain structure and function at 5 different time points. We adopted a novel multivariate approach that includes behavioural data and specific MRI protocols sensitive to tissue properties to assess local functional and structural and myelin changes over time. Our results show that while the reading network, located in the ventral occipitotemporal cortex, rapidly adapts to tactile input, sensory areas show changes in grey matter volume and intra-cortical myelin at different times. This approach has allowed us to examine and describe neuroplastic mechanisms underlying complex cognitive systems and their (sensory) inputs and (motor) outputs differentially, at a mesoscopic level.

Multimodal MRI assessment for first episode psychosis: A major change in the thalamus and an efficient stratification of a subgroup.

Multi-institutional brain imaging studies have emerged to resolve conflicting results among individual studies. However, adjusting multiple variables at the technical and cohort levels is challenging. Therefore, it is important to explore approaches that provide meaningful results from relatively small samples at institutional levels. We studied 87 first episode psychosis (FEP) patients and 62 healthy subjects by combining supervised integrated factor analysis (SIFA) with a novel pipeline for automated structure-based analysis, an efficient and comprehensive method for dimensional data reduction that our group recently established. We integrated multiple MRI features (volume, DTI indices, resting state fMRI-rsfMRI) in the whole brain of each participant in an unbiased manner. The automated structure-based analysis showed widespread DTI abnormalities in FEP and rs-fMRI differences between FEP and healthy subjects mostly centered in thalamus. The combination of multiple modalities with SIFA was more efficient than the use of single modalities to stratify a subgroup of FEP (individuals with schizophrenia or schizoaffective disorder) that had more robust deficits from the overall FEP group. The information from multiple MRI modalities and analytical methods highlighted the thalamus as significantly abnormal in FEP. This study serves as a proof-of-concept for the potential of this methodology to reveal disease underpins and to stratify populations into more homogeneous sub-groups.

Brain structural and functional alterations in MOG antibody disease.

BACKGROUND: The impact of myelin oligodendrocyte glycoprotein antibody disease (MOGAD) on brain structure and function is unknown. OBJECTIVES: The aim of this study was to study the multimodal brain MRI alterations in MOGAD and to investigate their clinical significance. METHODS: A total of 17 MOGAD, 20 aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders (AQP4 + NMOSD), and 28 healthy controls (HC) were prospectively recruited. Voxel-wise gray matter (GM) volume, fractional anisotropy (FA), mean diffusivity (MD), and degree centrality (DC) were compared between groups. Clinical associations and differential diagnosis were determined using partial correlation and stepwise logistic regression. RESULTS: In comparison with HC, MOGAD had GM atrophy in frontal and temporal lobe, insula, thalamus, and hippocampus, and WM fiber disruption in optic radiation and anterior/posterior corona radiata; DC decreased in cerebellum and increased in temporal lobe. Compared to AQP4 + NMOSD, MOGAD presented lower GM volume in postcentral gyrus and decreased DC in cerebellum. Hippocampus/parahippocampus atrophy associated with Expanded Disability Status Scale (R = -0.55, p = 0.04) and California Verbal Learning Test (R = 0.62, p = 0.031). The differentiation of MOGAD from AQP4 + NMOSD achieved an accuracy of 95% using FA in splenium of corpus callosum and DC in occipital gyrus. CONCLUSION: Distinct structural and functional alterations were identified in MOGAD. Hippocampus/parahippocampus atrophy associated with clinical disability and cognitive impairment.

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain.

Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.