Brain Parcellation Research Articles

DTI Measurements for Huntington Disease Using Mricloud

Aim: Neurodegenerative diseases are important health problems that affect many people. In this study, it was aimed to examine the brain regions of Huntington's patients by performing brain parcellation. Material and Method: 8 controls and 8 Huntington's patients participated in the study. We measured four Diffusion Tensor Imaging metrics which were axial diffusivity, mean diffusivity, radial diffusivity and fractional anisotropy performing brain parcellation over Diffusion Tensor Imaging for control and patient groups. We used a full automated data-driven approach to study the whole brain, divided in regions of interest using mricloud. Results: When the huntington disease group compared to control group, We found that mean diffusivity and axial diffusivity increased frontal, parietal, temporal, occipital, corpus callosum, white matter, limbic and subcortical structures, and radial diffusivity increased corpus callosum, capsula interna (p

Spectral Graph Neural Network-Based Multi-Atlas Brain Network Fusion for Major Depressive Disorder Diagnosis.

Major Depressive Disorder (MDD) imposes a substantial burden within the healthcare domain, impacting millions of individuals worldwide. Functional Magnetic Resonance Imaging (fMRI) has emerged as a promising tool for the objective diagnosis of MDD, enabling the investigation of functional connectivity patterns in the brain associated with this disorder. However, most existing methods focus on a single brain atlas, which limits their ability to capture the complex, multi-scale nature of functional brain networks. To address these limitations, we propose a novel multi-atlas fusion method that incorporates early and late fusion in a unified framework. Our method introduces the concept of the holistic Functional Connectivity Network (FCN), which captures both intra-atlas relationships within individual atlases and inter-regional relationships between atlases with different brain parcellation scales. This comprehensive representation enables the identification of potential disease-related patterns associated with MDD in the early stage of our framework. Moreover, by decoding the holistic FCN from various perspectives through multiple spectral Graph Convolutional Neural Networks and fusing their results with decision-level ensembles, we further improve the performance of MDD diagnosis. Our approach is easily implemented with minimal modifications to existing model structures and demonstrates a robust performance across different baseline models. Our method, evaluated on public resting-state fMRI datasets, surpasses the current multi-atlas fusion methods, enhancing the accuracy of MDD diagnosis. The proposed novel multi-atlas fusion framework provides a more reliable MDD diagnostic technique. Experimental results show our approach outperforms both single- and multi-atlas-based methods, demonstrating its effectiveness in advancing MDD diagnosis.

Functional MRI brain state occupancy in the presence of cerebral small vessel disease—a pre-registered replication analysis of the Hamburg City Health Study

Abstract We aimed to replicate recent findings on the association between the extent of cerebral small vessel disease (cSVD), functional brain network dedifferentiation, and cognitive impairment. We analyzed demographic, imaging, and behavioral data from the prospective population-based Hamburg City Health Study. Using a fully prespecified analysis pipeline, we estimated discrete brain states from structural and resting-state functional magnetic resonance imaging (MRI). In a multiverse analysis, we varied brain parcellations and functional MRI confound regression strategies. The severity of cSVD was operationalized as the volume of white matter hyperintensities of presumed vascular origin. Processing speed and executive dysfunction were quantified using the Trail Making Test (TMT). We hypothesized a) that a greater volume of supratentorial white matter hyperintensities would be associated with less time spent in functional MRI-derived brain states of high fractional occupancy; and b) that less time spent in these high-occupancy brain states associated with a longer time to completion in part B of the TMT. High-occupancy brain states were characterized by activation or suppression of the default mode network. Every 5.1-fold increase in WMH volume was associated with a 0.94-fold reduction in the odds of occupying DMN-related brain states (P = 5.01×10−8). Every 5% increase in time spent in high-occupancy brain states was associated with a 0.98-fold reduction in the TMT-B completion time (P = 0.0116). Findings were robust across most brain parcellations and confound regression strategies. In conclusion, we successfully replicated previous findings on the association between cSVD, functional brain occupancy, and cognition in an independent sample. The data provide further evidence for a functional network dedifferentiation hypothesis of cSVD-related cognitive impairment. Further research is required to elucidate the mechanisms underlying these associations.

A Comprehensive and Broad Approach to Resting-State Functional Connectivity in Adult Patients with Mild Traumatic Brain Injury.

Several recent works using resting-state fMRI suggest possible alterations of resting-state functional connectivity after mild traumatic brain injury. However, the literature is plagued by various analysis approaches and small study cohorts, resulting in an inconsistent array of reported findings. In this study, we aimed to investigate differences in whole-brain resting-state functional connectivity between adult patients with mild traumatic brain injury within 1 month of injury and healthy control subjects using several comprehensive resting-state functional connectivity measurement methods and analyses. A total of 123 subjects (72 patients with mild traumatic brain injury and 51 healthy controls) were included. A standard fMRI preprocessing pipeline was used. ROI/seed-based analyses were conducted using 4 standard brain parcellation methods, and the independent component analysis method was applied to measure resting-state functional connectivity. The fractional amplitude of low-frequency fluctuations was also measured. Group comparisons were performed on all measurements with appropriate whole-brain multilevel statistical analysis and correction. There were no significant differences in age, sex, education, and hand preference between groups as well as no significant correlation between all measurements and these potential confounders. We found that each resting-state functional connectivity measurement revealed various regions or connections that were different between groups. However, after we corrected for multiple comparisons, the results showed no statistically significant differences between groups in terms of resting-state functional connectivity across methods and analyses. Although previous studies point to multiple regions and networks as possible mild traumatic brain injury biomarkers, this study shows that the effect of mild injury on brain resting-state functional connectivity has not survived after rigorous statistical correction. A further study using subject-level connectivity analyses may be necessary due to both subtle and variable effects of mild traumatic brain injury on brain functional connectivity across individuals.

Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology

BackgroundAdolescence heralds the onset of considerable psychopathology, which may be conceptualized as an emergence of altered covariation between symptoms and brain measures. Multivariate methods can detect such modes of covariation or latent dimensions, but none specifically relating to psychopathology have yet been found using population-level structural brain data. Using voxelwise (instead of parcellated) brain data may strengthen latent dimensions’ brain-psychosocial relationships, but this creates computational challenges. MethodsWe obtained voxelwise gray matter density and psychosocial variables from the baseline (ages 9–10 years) Adolescent Brain Cognitive Development (ABCD) Study cohort (N = 11,288) and employed a state-of-the-art segmentation method, sparse partial least squares, and a rigorous machine learning framework to prevent overfitting. ResultsWe found 6 latent dimensions, 4 of which pertain specifically to mental health. The mental health dimensions were related to overeating, anorexia/internalizing, oppositional symptoms (all ps < .002) and attention-deficit/hyperactivity disorder symptoms (p = .03). Attention-deficit/hyperactivity disorder was related to increased and internalizing symptoms related to decreased gray matter density in dopaminergic and serotonergic midbrain areas, whereas oppositional symptoms were related to increased gray matter in a noradrenergic nucleus. Internalizing symptoms were related to increased and oppositional symptoms to reduced gray matter density in the insular, cingulate, and auditory cortices. Striatal regions featured strongly, with reduced caudate nucleus gray matter in attention-deficit/hyperactivity disorder and reduced putamen gray matter in oppositional/conduct problems. Voxelwise gray matter density generated stronger brain-psychosocial correlations than brain parcellations. ConclusionsVoxelwise brain data strengthen latent dimensions of brain-psychosocial covariation, and sparse multivariate methods increase their psychopathological specificity. Internalizing and externalizing symptoms are associated with opposite gray matter changes in similar cortical and subcortical areas.

Longitudinal stability of cortical grey matter measures varies across brain regions, imaging metrics, and testing sites in the ABCD study

Abstract Magnetic resonance imaging (MRI) is a vital tool for the study of brain structure and function. It is increasingly being used in individual differences research to examine brain-behaviour associations. Prior work has demonstrated low test-retest stability of functional MRI measures, highlighting the need to examine the longitudinal stability (test-retest reliability across long timespans) of MRI measures across brain regions and imaging metrics, particularly in adolescence. In this study, we examined the longitudinal stability of grey matter measures (cortical thickness, surface area, and volume) across brain regions, and testing sites in the Adolescent Brain Cognitive Development (ABCD) study release v4.0. Longitudinal stability ICC estimates ranged from 0 to .98, depending on the measure, parcellation, and brain region. We used Intra-Class Effect Decomposition (ICED) to estimate between-subjects variance and error variance, and assess the relative contribution of each across brain regions and testing sites on longitudinal stability. In further exploratory analyses, we examined the influence of parcellation used (Desikan-Killiany-Tourville and Destrieux) on longitudinal stability. Our results highlight meaningful heterogeneity in longitudinal stability across brain regions, structural measures (cortical thickness in particular), parcellations, and ABCD testing sites. Differences in longitudinal stability across brain regions were largely driven by between-subjects variance, whereas differences in longitudinal stability across testing sites were largely driven by differences in error variance. We argue that investigations such as this are essential to capture patterns of longitudinal stability heterogeneity that would otherwise go undiagnosed. Such improved understanding allows the field to more accurately interpret results, compare effect sizes, and plan more powerful studies.

Updates to the Melbourne Children’s Regional Infant Brain Software Package (M-CRIB-S)

The delineation of cortical areas on magnetic resonance images (MRI) is important for understanding the complexities of the developing human brain. The previous version of the Melbourne Children's Regional Infant Brain (M-CRIB-S) (Adamson et al. Scientific Reports, 10(1), 10, 2020) is a software package that performs whole-brain segmentation, cortical surface extraction and parcellation of the neonatal brain. Available cortical parcellation schemes in the M-CRIB-S are the adult-compatible 34- and 31-region per hemisphere Desikan-Killiany (DK) and Desikan-Killiany-Tourville (DKT), respectively. We present a major update to the software package which achieves two aims: 1) to make the voxel-based segmentation outputs derived from the Freesurfer-compatible M-CRIB scheme, and 2) to improve the accuracy of whole-brain segmentation and cortical surface extraction. Cortical surface extraction has been improved with additional steps to improve penetration of the inner surface into thin gyri. The improved cortical surface extraction is shown to increase the robustness of measures such as surface area, cortical thickness, and cortical volume.

DDParcel: Deep Learning Anatomical Brain Parcellation From Diffusion MRI.

Parcellation of anatomically segregated cortical and subcortical brain regions is required in diffusion MRI (dMRI) analysis for region-specific quantification and better anatomical specificity of tractography. Most current dMRI parcellation approaches compute the parcellation from anatomical MRI (T1- or T2-weighted) data, using tools such as FreeSurfer or CAT12, and then register it to the diffusion space. However, the registration is challenging due to image distortions and low resolution of dMRI data, often resulting in mislabeling in the derived brain parcellation. Furthermore, these approaches are not applicable when anatomical MRI data is unavailable. As an alternative we developed the Deep Diffusion Parcellation (DDParcel), a deep learning method for fast and accurate parcellation of brain anatomical regions directly from dMRI data. The input to DDParcel are dMRI parameter maps and the output are labels for 101 anatomical regions corresponding to the FreeSurfer Desikan-Killiany (DK) parcellation. A multi-level fusion network leverages complementary information in the different input maps, at three network levels: input, intermediate layer, and output. DDParcel learns the registration of diffusion features to anatomical MRI from the high-quality Human Connectome Project data. Then, to predict brain parcellation for a new subject, the DDParcel network no longer requires anatomical MRI data but only the dMRI data. Comparing DDParcel's parcellation with T1w-based parcellation shows higher test-retest reproducibility and a higher regional homogeneity, while requiring much less computational time. Generalizability is demonstrated on a range of populations and dMRI acquisition protocols. Utility of DDParcel's parcellation is demonstrated on tractography analysis for fiber tract identification.

A New Multi-Atlas Based Deep Learning Segmentation Framework With Differentiable Atlas Feature Warping.

Deep learning based multi-atlas segmentation (DL-MA) has achieved the state-of-the-art performance in many medical image segmentation tasks, e.g., brain parcellation. In DL-MA methods, atlas-target correspondence is the key for accurate segmentation. In most existing DL-MA methods, such correspondence is usually established using traditional or deep learning based registration methods at image level with no further feature level adaption. This could cause possible atlas-target feature inconsistency. As a result, the information from atlases often has limited positive and even counteractive impact on the final segmentation results. To tackle this issue, in this paper, we propose a new DL-MA framework, where a novel differentiable atlas feature warping module with a new smooth regularization term is presented to establish feature level atlas-target correspondence. Comparing with the existing DL-MA methods, in our framework, atlas features containing anatomical prior knowledge are more relevant to the target image feature, leading the final segmentation results to a high accuracy level. We evaluate our framework in the context of brain parcellation using two public MR brain image datasets: LPBA40 and NIREP-NA0. The experimental results demonstrate that our framework outperforms both traditional multi-atlas segmentation (MAS) and state-of-the-art DL-MA methods with statistical significance. Further ablation studies confirm the effectiveness of the proposed differentiable atlas feature warping module.

Prior-guided individualized thalamic parcellation based on local diffusion characteristics.

Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior-guided parcellation (PG-par) method to achieve robust individualized thalamic parcellation based on a central-boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high-quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high-probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test-retest assessment, the proposed prior-guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG-par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior-guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior-guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.

Impact of brain parcellation on prediction performance in models of cognition and demographics.

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

Neuroanatomical comparison of treatment-resistant and treatment-responsive schizophrenia patients using the cloud-based brain magnetic resonance image segmentation and parcellation system: An MRIcloud study

Recent developments in neuroimaging have improved our understanding of the biological mechanisms underlying schizophrenia. However, neuroimaging findings in treatment-resistant schizophrenia (TRS) remain unclear. In the present study, we aimed to explore potential neuroanatomical regions that may be associated with treatment resistance in schizophrenia patients by comparing neuroanatomical regions of TRS and non-TRS patients using the MRICloud method. A total of 33 schizophrenia patients (meeting DSM 5 diagnostic criteria for schizophrenia) were included in the study. Patients were dichotomized into TRS (n = 18) and non-TRS (n = 15) groups, and all patients underwent MRI. Neuroanatomical regions of TRS and non-TRS patients were compared using the MRICloud method. Disease severity was measured using the Positive and Negative Syndrome Scale (PANSS). Interestingly, a statistically significant greater left Corpus Collosum (CC) thickness was found in TRS patients compared to non-TRS patients. It is clear that further studies comparing TRS patients with non-TRS patients are needed, and these studies should focus on the circuits in the corpus callosum that are thought to play a role in treatment resistance. Further longitudinal studies are also needed to complement the cross-sectional studies, using a multimodal imaging approach in the patients with clearly defined TRS criteria.

Development of the next-generation functional neuro-cognitive imaging protocol - Part 1: A 3D sliding-window convolutional neural net for automated brain parcellation

Functional MRI has emerged as a powerful tool to assess the severity of Post-concussion syndrome (PCS) and to provide guidance for neuro-cognitive therapists during treatment. The next-generation functional neuro-cognitive imaging protocol (fNCI2) has been developed to provide this assessment. This paper covers the first step in the analysis process, the development of a rapidly re-trainable, machine-learning, brain parcellation tool. The use of a sufficiently deep U-Net architecture encompassing a small (39 × 39 × 39 voxel input, 27 × 27 × 27 voxel output) sliding window to sample the entirety of the 3D image allows for the prediction of the entire image using only a single trained network. A large number of training, validating, and testing windows are thus generated from the 101 manually-labeled Mindboggle images, and full-image prediction is provided via a voxel-vote method using overlapping windows. Our method produces parcellated images that are highly consistent with standard atlas-based methods in under 3 min on a modern GPU, and the single network architecture allows for rapid retraining (<36 hr) as needed.

Brain microstructure and connectivity in COVID-19 patients with olfactory or cognitive impairment

IntroductionThe COVID-19 pandemic has affected millions worldwide, causing mortality and multi-organ morbidity. Neurological complications have been recognized. This study aimed to assess brain structural, microstructural, and connectivity alterations in patients with COVID-19-related olfactory or cognitive impairment using post-acute (time from onset: 264[208–313] days) multi-directional diffusion-weighted MRI (DW-MRI). MethodsThe study included 16 COVID-19 patients with cognitive impairment (COVID-CM), 35 COVID-19 patients with olfactory disorder (COVID-OD), and 14 controls. A state-of-the-art processing pipeline was developed for DW-MRI pre-processing, mean diffusivity and fractional anisotropy computation, fiber density and cross-section analysis, and tractography of white-matter bundles. Brain parcellation required for probing network connectivity, region-specific microstructure and volume, and cortical thickness was based on T1-weighted scans and anatomical atlases. ResultsCompared to controls, COVID-CM patients showed overall gray matter atrophy (age and sex corrected p = 0.004), and both COVID-19 patient groups showed regional atrophy and cortical thinning. Both groups presented an increase in gray matter mean diffusivity (corrected p = 0.001), decrease in white matter fiber density and cross-section (corrected p < 0.05), , and COVID-CM patients also displayed an overall increased diffusivity (p = 0.022) and decreased anisotropy (corrected p = 0.038) in white matter. Graph-based analysis revealed reduced network modularity, with an extensive pattern of connectivity increase, in conjunction with a localized reduction in a few connections, mainly located in the left hemisphere. The left cingulate, anterior cingulate, and insula were primarily involved. ConclusionExpanding upon previous findings, this study further investigated significant alterations in brain morphology, microstructure, and connectivity in COVID-19 patients with olfactory or cognitive disfunction. These findings suggest underlying neurodegeneration, neuroinflammation, and concomitant compensatory mechanisms. Future longitudinal studies are required to monitor the alterations over time and assess their transient or permanent nature.

Hierarchical Graph Convolutional Network Built by Multiscale Atlases for Brain Disorder Diagnosis Using Functional Connectivity.

Functional connectivity network (FCN) data from functional magnetic resonance imaging (fMRI) is increasingly used for the diagnosis of brain disorders. However, state-of-the-art studies used to build the FCN using a single brain parcellation atlas at a certain spatial scale, which largely neglected functional interactions across different spatial scales in hierarchical manners. In this study, we propose a novel framework to perform multiscale FCN analysis for brain disorder diagnosis. We first use a set of well-defined multiscale atlases to compute multiscale FCNs. Then, we utilize biologically meaningful brain hierarchical relationships among the regions in multiscale atlases to perform nodal pooling across multiple spatial scales, namely "Atlas-guided Pooling (AP)." Accordingly, we propose a multiscale-atlases-based hierarchical graph convolutional network (MAHGCN), built on the stacked layers of graph convolution and the AP, for a comprehensive extraction of diagnostic information from multiscale FCNs. Experiments on neuroimaging data from 1792 subjects demonstrate the effectiveness of our proposed method in the diagnoses of Alzheimer's disease (AD), the prodromal stage of AD i.e., mild cognitive impairment (MCI), as well as autism spectrum disorder (ASD), with the accuracy of 88.9%, 78.6%, and 72.7%, respectively. All results show significant advantages of our proposed method over other competing methods. This study not only demonstrates the feasibility of brain disorder diagnosis using resting-state fMRI empowered by deep learning but also highlights that the functional interactions in the multiscale brain hierarchy are worth being explored and integrated into deep learning network architectures for a better understanding of the neuropathology of brain disorders. The codes for MAHGCN are publicly available at "https://github.com/MianxinLiu/ MAHGCN-code."

The effect of using group-averaged or individualized brain parcellations when investigating connectome dysfunction in psychosis.

Functional magnetic resonance imaging (fMRI) is widely used to investigate functional coupling (FC) disturbances in a range of clinical disorders. Most analyses performed to date have used group-based parcellations for defining regions of interest (ROIs), in which a single parcellation is applied to each brain. This approach neglects individual differences in brain functional organization and may inaccurately delineate the true borders of functional regions. These inaccuracies could inflate or underestimate group differences in case-control analyses. We investigated how individual differences in brain organization influence group comparisons of FC using psychosis as a case study, drawing on fMRI data in 121 early psychosis patients and 57 controls. We defined FC networks using either a group-based parcellation or an individually tailored variant of the same parcellation. Individualized parcellations yielded more functionally homogeneous ROIs than did group-based parcellations. At the level of individual connections, case-control FC differences were widespread, but the group-based parcellation identified approximately 7.7% more connections as dysfunctional than the individualized parcellation. When considering differences at the level of functional networks, the results from both parcellations converged. Our results suggest that a substantial fraction of dysconnectivity previously observed in psychosis may be driven by the parcellation method, rather than by a pathophysiological process related to psychosis.

Application of deep learning in fMRI-based human brain parcellation: a review

Abstract Functional magnetic resonance imaging (fMRI)-based human brain parcellation reveals brain fundamental organizational principles noninvasively, providing prior guidance for functional analysis and physiological measurement of the brain. Recently, the profound success of deep learning in natural and medical images has attracted an increasing number of deep learning-based investigations for brain parcellation which have accomplished encouraging achievements. This review aims to provide researchers with a comprehensive overview of deep learning-based fMRI brain parcellation and promote the development of this emerging frontier. To begin, we present the history of brain parcellation, emphasizing the current advantages of deep learning in this field. Following a summary of the input modalities, traditional methods, and evaluation criteria for fMRI-based brain parcellation, we comprehensively review the current deep-learning approaches and categorize them based on network architectures. The datasets, models, performance, advantages and limitations are described in detail. Finally, we discuss the current challenges and future directions in methodology, neuroscience, and application, to inspire future investigation in deep learning-based brain parcellation.

Genetic basis of anatomical asymmetry and aberrant dynamic functional networks in Alzheimer's disease.

Genetic associations with macroscopic brain networks can provide insights into healthy and aberrant cortical connectivity in disease. However, associations specific to dynamic functional connectivity in Alzheimer's disease are still largely unexplored. Understanding the association between gene expression in the brain and functional networks may provide useful information about the molecular processes underlying variations in impaired brain function. Given the potential of dynamic functional connectivity to uncover brain states associated with Alzheimer's disease, it is interesting to ask: How does gene expression associated with Alzheimer's disease map onto the dynamic functional brain connectivity? If genetic variants associated with neurodegenerative processes involved in Alzheimer's disease are to be correlated with brain function, it is essential to generate such a map. Here, we investigate how the relation between gene expression in the brain and dynamic functional connectivity arises from nodal interactions, quantified by their role in network centrality (i.e. the drivers of the metastability), and the principal component of genetic co-expression across the brain. Our analyses include genetic variations associated with Alzheimer's disease and also genetic variants expressed within the cholinergic brain pathways. Our findings show that contrasts in metastability of functional networks between Alzheimer's and healthy individuals can in part be explained by the two combinations of genetic co-variations in the brain with the confidence interval between 72% and 92%. The highly central nodes, driving the brain aberrant metastable dynamics in Alzheimer's disease, highly correlate with the magnitude of variations from two combinations of genes expressed in the brain. These nodes include mainly the white matter, parietal and occipital brain regions, each of which (or their combinations) are involved in impaired cognitive function in Alzheimer's disease. In addition, our results provide evidence of the role of genetic associations across brain regions in asymmetric changes in ageing. We validated our findings on the same cohort using alternative brain parcellation methods. This work demonstrates how genetic variations underpin aberrant dynamic functional connectivity in Alzheimer's disease.

Imaging diagnosis and emotion recognition relationship in mild cognitive impairment

AbstractBackgroundRecent research has focused attention on social cognition impairment in Alzheimer Disease (AD). Specifically, emotion recognition can be evaluated with Reading the Mind in the Eyes Test (RMET). Some neuroanatomical changes could be related with social and cognitive deficits in patients. Therefore, future research combining cognitive indicators (RMET) with brain structure and function parameters (volumes, glucose metabolism, blood flow, amyloid deposits…) is required.MethodParticipants are patients from the Neurology Unit (Hospital Universitari i Politècnic La Fe). They were neuropsychologically evaluated, and CSF samples were obtained to determine some biomarkers (β‐amyloid‐42, β‐amyloid‐40, p‐Tau…). Also, brain imaging techniques (magnetic resonance (MR) and Positron Emission Tomography with [18F]Fluorodeoxyglucose (FDG‐PET/CT)) were applied to obtain brain structural and functional information, and to evaluate the relationship between them and emotion recognition evaluation (RMET). Imaging data were processed following SPM and CAT12 pipelines, a brain parcellation based on the Hammers atlas was used. Finally, 166 patients (with MR and/or FDG‐PET/CT) diagnosed with mild cognitive impairment (MCI) were included in the study.ResultThe correlation analysis between FDG‐PET/CT and RMET results showed significant correlations. Specifically, with prefrontal medial L and R (r = 0.36), posterior cingulate L and R (r = 0.34), parietal inferior L and R (r = 0.31), anterior cingulate L and R (r = 0.32), prefrontal lateral L and R (r = 0.32), insula R (r = 0.31) and basal ganglia R (r = 0.3). So, they are considered potential brain function biomarkers in MCI. Regarding the correlation analysis between MR (GM volumes (TIV‐corrected)) and RMET results, some significant correlations were observed. Specifically, with the straight gyrus L (r = 0.40), superior frontal gyrus L (r = 0.37), insula anterior inferior cortex R (r = 0.36), fusiform gyrus R (r = 0.36), anterior cingulate gyrus R (r = 0.36), insula anterior short gyrus L (r = 0.36), posterior and anterior cingulate gyrus L (r = 0.36, 0.35), and anterior orbital gyrus L (r = 0.35). So, they are considered potential brain structure biomarkers in MCI.ConclusionThe relationship between brain imaging parameters and emotion recognition impairment variables could provide important information for further biomarkers identification, as well as to identify potential physiopathological mechanisms involved in neurodegenerative development since early steps.

Should one go for individual- or group-level brain parcellations? A deep-phenotyping benchmark.

The analysis and understanding of brain characteristics often require considering region-level information rather than voxel-sampled data. Subject-specific parcellations have been put forward in recent years, as they can adapt to individual brain organization and thus offer more accurate individual summaries than standard atlases. However, the price to pay for adaptability is the lack of group-level consistency of the data representation. Here, we investigate whether the good representations brought by individualized models are merely an effect of circular analysis, in which individual brain features are better represented by subject-specific summaries, or whether this carries over to new individuals, i.e., whether one can actually adapt an existing parcellation to new individuals and still obtain good summaries in these individuals. For this, we adapt a dictionary-learning method to produce brain parcellations. We use it on a deep-phenotyping dataset to assess quantitatively the patterns of activity obtained under naturalistic and controlled-task-based settings. We show that the benefits of individual parcellations are substantial, but that they vary a lot across brain systems.