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Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two‐fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality‐wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed‐forward network, an autoencoder, a bi‐directional long short‐term memory unit with attention as the features extractor, and a linear attention module for controlling modality‐specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state‐of‐the‐art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms. Multimodal deep learning of imaging genetics data for characterizing mental disorders. The model processes multisource physiological data. It boosts the schizophrenia classifications accuracy by a margin compared with the existing model. The interpretation provides a handful of neurological ad genomics features that explains the underlying mechanism of the disease.

Multimodal learning has emerged as a powerful technique that leverages diverse data sources to enhance learning and decision‐making processes. Adapting this approach to analyzing data collected from different biological domains is intuitive, especially for studying neuropsychiatric disorders. A complex neuropsychiatric disorder like schizophrenia (SZ) can affect multiple aspects of the brain and biologies. These biological sources each present distinct yet correlated expressions of subjects' underlying physiological processes. Joint learning from these data sources can improve our understanding of the disorder. However, combining these biological sources is challenging for several reasons: (i) observations are domain specific, leading to data being represented in dissimilar subspaces, and (ii) fused data are often noisy and high‐dimensional, making it challenging to identify relevant information. To address these challenges, we propose a multimodal artificial intelligence model with a novel fusion module inspired by a bottleneck attention module. We use deep neural networks to learn latent space representations of the input streams. Next, we introduce a two‐dimensional (spatio‐modality) attention module to regulate the intermediate fusion for SZ classification. We implement spatial attention via a dilated convolutional neural network that creates large receptive fields for extracting significant contextual patterns. The resulting joint learning framework maximizes complementarity allowing us to explore the correspondence among the modalities. We test our model on a multimodal imaging‐genetic dataset and achieve an SZ prediction accuracy of 94.10% (p < .0001), outperforming state‐of‐the‐art unimodal and multimodal models for the task. Moreover, the model provides inherent interpretability that helps identify concepts significant for the neural network's decision and explains the underlying physiopathology of the disorder. Results also show that functional connectivity among subcortical, sensorimotor, and cognitive control domains plays an important role in characterizing SZ. Analysis of the spatio‐modality attention scores suggests that structural components like the supplementary motor area, caudate, and insula play a significant role in SZ. Biclustering the attention scores discover a multimodal cluster that includes genes CSMD1, ATK3, MOB4, and HSPE1, all of which have been identified as relevant to SZ. In summary, feature attribution appears to be especially useful for probing the transient and confined but decisive patterns of complex disorders, and it shows promise for extensive applicability in future studies. Attentive fusion of neuroimaging and genomics data classify schizophrenia (SZ) with high precision. The attention scores provide the most contributing imaging‐genetics features for characterizing the disorder. The proposed fusion module is self‐explaining; interprets how each biological sources complement the other and leverage their combination to better understand SZ.

Head motion systematically distorts clinical and research MRI data. Motion artifacts have biased findings from many structural and functional brain MRI studies. An effective way to remove motion artifacts is to exclude MRI data frames affected by head motion. However, such post-hoc frame censoring can lead to data loss rates of 50% or more in our pediatric patient cohorts. Hence, many scanner operators collect additional ‘buffer data’, an expensive practice that, by itself, does not guarantee sufficient high-quality MRI data for a given participant. Therefore, we developed an easy-to-setup, easy-to-use Framewise Integrated Real-time MRI Monitoring (FIRMM) software suite that provides scanner operators with head motion analytics in real-time, allowing them to scan each subject until the desired amount of low-movement data has been collected. Our analyses show that using FIRMM to identify the ideal scan time for each person can reduce total brain MRI scan times and associated costs by 50% or more. [Display omitted]

•This article covers multiple aspects of imaging perivascular spaces (PVS) in humans with brain MRI, including acquisition protocols, processing methods, and the advantages and pitfalls of these strategies.•This article summarizes techniques to quantify morphological and functional characteristics of PVS using brain structural and diffusion MRI.•This article reviews the results from human neuroimaging studies pertaining PVS both across the normative lifespan and in neurological conditions. In this article, we provide an overview of current neuroimaging methods for studying perivascular spaces (PVS) in humans using brain MRI. In recent years, an increasing number of studies highlighted the role of PVS in cerebrospinal/interstial fluid circulation and clearance of cerebral waste products and their association with neurological diseases. Novel strategies and techniques have been introduced to improve the quantification of PVS and to investigate their function and morphological features in physiological and pathological conditions. After a brief introduction on the anatomy and physiology of PVS, we examine the latest technological developments to quantitatively analyze the structure and function of PVS in humans with MRI. We describe the applications, advantages, and limitations of these methods, providing guidance and suggestions on the acquisition protocols and analysis techniques that can be applied to study PVS in vivo. Finally, we review the human neuroimaging studies on PVS across the normative lifespan and in the context of neurological disorders.

Traditional neuroimage analysis pipelines involve computationally intensive, time-consuming optimization steps, and thus, do not scale well to large cohort studies with thousands or tens of thousands of individuals. In this work we propose a fast and accurate deep learning based neuroimaging pipeline for the automated processing of structural human brain MRI scans, replicating FreeSurfer’s anatomical segmentation including surface reconstruction and cortical parcellation. To this end, we introduce an advanced deep learning architecture capable of whole-brain segmentation into 95 classes. The network architecture incorporates local and global competition via competitive dense blocks and competitive skip pathways, as well as multi-slice information aggregation that specifically tailor network performance towards accurate segmentation of both cortical and subcortical structures. Further, we perform fast cortical surface reconstruction and thickness analysis by introducing a spectral spherical embedding and by directly mapping the cortical labels from the image to the surface. This approach provides a full FreeSurfer alternative for volumetric analysis (in under 1 ​min) and surface-based thickness analysis (within only around 1 ​h runtime). For sustainability of this approach we perform extensive validation: we assert high segmentation accuracy on several unseen datasets, measure generalizability and demonstrate increased test-retest reliability, and high sensitivity to group differences in dementia. [Display omitted]

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects approximately 8%-12% of children worldwide. Throughout an individual's lifetime, ADHD can significantly increase risk for other psychiatric disorders, educational and occupational failure, accidents, criminality, social disability and addictions. No single risk factor is necessary or sufficient to cause ADHD. The multifactorial causation of ADHD is reflected in the heterogeneity of this disorder, as indicated by its diversity of psychiatric comorbidities, varied clinical profiles, patterns of neurocognitive impairment and developmental trajectories, and the wide range of structural and functional brain anomalies. Although evidence-based treatments can reduce ADHD symptoms in a substantial portion of affected individuals, there is yet no curative treatment for ADHD. A number of theoretical models of the emergence and developmental trajectories of ADHD have been proposed, aimed at providing systematic guides for clinical research and practice. We conducted a comprehensive review of the current status of research in understanding the heterogeneity of ADHD in terms of etiology, clinical profiles and trajectories, and neurobiological mechanisms. We suggest that further research focus on investigating the impact of the etiological risk factors and their interactions with developmental neural mechanisms and clinical profiles in ADHD. Such research would have heuristic value for identifying biologically homogeneous subgroups and could facilitate the development of novel and more tailored interventions that target underlying neural anomalies characteristic of more homogeneous subgroups.

Abstract Widespread structural brain abnormalities have been consistently reported in schizophrenia, but their relation to the heterogeneous clinical manifestations remains unknown. In particular, it is unclear whether anatomical abnormalities in discrete regions give rise to discrete symptoms or whether distributed abnormalities give rise to the broad clinical profile associated with schizophrenia. Here, we apply a multivariate data-driven approach to investigate covariance patterns between multiple-symptom domains and distributed brain abnormalities in schizophrenia. Structural magnetic resonance imaging and clinical data were derived from one discovery sample (133 patients and 113 controls) and one independent validation sample (108 patients and 69 controls). Disease-related voxel-wise brain abnormalities were estimated using deformation-based morphometry. Partial least-squares analysis was used to comprehensively map clinical, neuropsychological, and demographic data onto distributed deformation in a single multivariate model. The analysis identified 3 latent clinical-anatomical dimensions that collectively accounted for 55% of the covariance between clinical data and brain deformation. The first latent clinical-anatomical dimension was replicated in an independent sample, encompassing cognitive impairments, negative symptom severity, and brain abnormalities within the default mode and visual networks. This cognitive-negative dimension was associated with low socioeconomic status and was represented across multiple races. Altogether, we identified a continuous cognitive-negative dimension of schizophrenia, centered on 2 intrinsic networks. By simultaneously taking into account both clinical manifestations and neuroanatomical abnormalities, the present results open new avenues for multi-omic stratification and biotyping of individuals with schizophrenia.

Animal and human autopsy studies suggest that subfields of the hippocampal formation are differentially affected by neuropsychiatric diseases. Therefore, subfield volumes may be more sensitive to effects of disease processes. The few human studies that segmented subfields of the hippocampal formation in vivo either assessed the subfields only in the body of the hippocampus, assessed only three subfields, or did not take the differential angulation of the head of the hippocampus into account. We developed a protocol using 7Tesla MRI with isotropic voxels to reliably delineate the entorhinal cortex (ERC), subiculum (SUB), CA1, CA2, CA3, dentate gyrus (DG)&CA4 along the full-length of the hippocampus. Fourteen subjects (aged 54–74years, 2 men and 12 women) were scanned with a 3D turbo spin echo (TSE) sequence with isotropic voxels of 0.7×0.7×0.7mm3 on a 7T MRI whole body scanner. Based on previous protocols and extensive anatomic atlases, a new protocol for segmentation of subfields of the hippocampal formation was formulated. ERC, SUB, CA1, CA2, CA3 and DG&CA4 were manually segmented twice by one rater from coronal MR images. Good-to-excellent consistency was found for all subfields (Intraclass Correlation Coefficient's (ICC) varying from 0.74 to 0.98). Accuracy as measured with the Dice Similarity Index (DSI) was above 0.82 for all subfields, with the exception of the smaller subfield CA3 (0.68–0.70). In conclusion, this study shows that it is possible to delineate the main subfields of the hippocampal formation along its full-length in vivo at 7T MRI. Our data give evidence that this can be done in a reliable manner. Segmentation of subfields in the full-length of the hippocampus may bolster the study of the etiology neuropsychiatric diseases. ► We developed a protocol for subfield volumetry of the hippocampal formation at 7T. ► Innovations were use of thin-slice 7T data and inclusion of the hippocampal head. ► This protocol can be applied in consistent manner as shown by high ICC’s and DSI’s. ► This protocol may bolster the study of the etiology of neuropsychiatric diseases.

[This corrects the article DOI: 10.3389/fnhum.2025.1613084.].

As severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 nonvaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean ± SD, 55.54 ± 7.07; median 9.7 mo after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 ± 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion MRI measures of white matter microstructure, cortical thickness, white matter hyperintensity load, and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity (MD) and extracellular free water which were elevated in the white matter of post-SARS-CoV-2 individuals compared to matched controls (free water: 0.148 ± 0.018 vs. 0.142 ± 0.017, P < 0.001; MD [10−3 mm²/s]: 0.747 ± 0.021 vs. 0.740 ± 0.020, P < 0.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure, or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed.