Imaging structural co-variance between human brain regions

Multiomics Reveals Biological Mechanisms Linking Macroscale Structural Covariance Network Dysfunction With Neuropsychiatric Symptoms Across the Alzheimer's Disease Continuum

BACKGROUND

The high heterogeneity of neuropsychiatric symptoms (NPSs) hinders further exploration of their role in neurobiological mechanisms and Alzheimer's disease (AD). We aimed to delineate NPS patterns based on brain macroscale connectomics to understand the biological mechanisms of NPSs on the AD continuum.

METHODS

We constructed regional radiomics similarity networks for 550 participants (AD with NPSs [n = 376], AD without NPSs [n = 111], and normal control participants [n = 63]) from the CIBL (Chinese Imaging, Biomarkers, and Lifestyle) study. We identified regional radiomics similarity network connections associated with NPSs and then clustered distinct subtypes of AD with NPSs. An independent dataset (n = 189) and internal validation were performed to assess the robustness of the NPS subtypes. Subsequent multiomics analysis was performed to assess the distinct clinical phenotype and biological mechanisms in each NPS subtype.

RESULTS

AD patients with NPSs were clustered into severe (n = 187), moderate (n = 87), and mild (n = 102) NPS subtypes, each exhibiting distinct brain network dysfunction patterns. A high level of consistency in clustering NPSs was internally and externally validated. Severe and moderate NPS subtypes were associated with significant cognitive impairment, increased plasma p-tau181 (tau phosphorylated at threonine 181) levels, extensive decreased brain volume and cortical thickness, and accelerated cognitive decline. Gene set enrichment analysis revealed enrichment of differentially expressed genes in ion transport and synaptic transmission with variations for each NPS subtype. Genome-wide association study analysis defined the specific gene loci for each subtype of AD with NPSs (e.g., logical memory), consistent with clinical manifestations and progression patterns.

CONCLUSIONS

This study identified and validated 3 distinct NPS subtypes, underscoring the role of NPSs in neurobiological mechanisms and progression of the AD continuum.

Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network

BACKGROUND

Mild cognitive impairment (MCI) is a heterogeneous disorder with significant individual variabilities in clinical and biological features. Abnormal inter-regional structural covariance suggests disruption of the brain structural network in MCI. Most studies have examined group-level structural covariance alterations while ignoring individual-level differences. Hence, we aimed to investigate the heterogeneity of MCI using individual differential structural covariance network (IDSCN) analysis.

METHODS

T1-weighted images of 596 MCI patients and 309 cognitively normal (CN) were collected from the ADNI database as discovery dataset, and 122 MCI and 117 CN from the OASIS-3 dataset as validation cohort. We constructed each patient's IDSCN using regional gray matter volume and applied K-means clustering analysis to identify MCI subtypes based on significantly altered covariance edges. Then, clinical features, brain structure, and gene expression profiles were evaluated for each subtype.

RESULTS

In the ADNI dataset, MCI patients exhibited significant alterations in structural covariance edges, mainly involving the hippocampus, parahippocampal gyrus, and amygdala. Two robust MCI subtypes were identified. Subtype 1 showed faster disease progression relative to subtype 2, which was validated in the independent OASIS-3 dataset. Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal ion transport and neuron projection development. Finally, correlation analysis and functional annotation further revealed that the affected edges were related to cognitive performance and implicated in memory and emotion terms.

CONCLUSIONS

In summary, these findings offer new perspectives into understanding the heterogeneity of MCI and facilitate strategies for future precision medicine.

Alterations of gray matter volume and structural covariance network in unilateral frontal lobe low-grade gliomas

PURPOSE

To explore the alterations of gray matter volume (GMV) and structural covariant network (SCN) in unilateral frontal lobe low-grade gliomas (FLGGs).

MATERIALS AND METHODS

The three dimensional (3D) T1 structural images of 117 patients with unilateral FLGGs and 68 age- and sex-matched healthy controls (HCs) were enrolled. The voxel-based morphometry (VBM) analysis and graph theoretical analysis of SCN were conducted to investigate the impact of unilateral FLGGs on the brain structure. This represents the first structural MRI study integrating both voxel-level morphometric changes and network-level reorganization patterns in unilateral FLGGs.

RESULTS

Through VBM analysis, we found that unilateral FLGGs can cause increased GMV in contralesional amygdala, calcarine, and angular gyrus, ipsilesional amygdala as well as vermis_6. The SCN of contralesional cerebrum, ipsilesional unaffected regions and cerebellum in both patients and HCs have typical small-world properties (Sigma > 1, Lambda ≈ 1 and Gamma > 1). Compared to HCs, global and nodal network metrics changed significantly in patients.

CONCLUSION

The combination of VBM and SCN analysis revealed both focal GMV enlargement and topological alterations in patients with unilateral FLGGs, and provide a novel perspective of cross regional morphological collaborative changes for understanding the glioma-related neuroadaptation. These findings may suggest potential neuroimaging correlates of adaptive changes, which could inform future investigations into personalized treatment approaches.

CLINICAL TRIAL NUMBER

Not applicable.

Changes in topological properties of brain structural covariance networks and alertness in temporal lobe epilepsy with and without focal to bilateral tonic-clonic seizures

This study investigated brain structural covariance network (SCN) topological changes and alertness in temporal lobe epilepsy (TLE) with and without focal to bilateral tonic-clonic seizures (FBTCS). Seventy-eight subjects, including 32 TLE patients with FBTCS (TLE-FBTCS), 46 TLE patients without FBTCS (TLE-FS), and 42 healthy controls (HCs), underwent the Attention Network Test to assess alertness and volumetric MRI scans. SCNs were constructed and analyzed using graph theory. Results showed that TLE-FS patients had lower total cerebral volume than HCs, and the lowest volume was observed in the TLE-FBTCS group. Compared to HCs and TLE-FBTCS patients, TLE-FS patients exhibited increased small-worldness, normalized clustering coefficient, global efficiency, and modularity, but decreased normalized characteristic shortest path length and assortativity. Specific brain regions, such as the hippocampus, thalamus, and superior temporal sulcus, showed changes in nodal clustering coefficients and efficiency in TLE-FS patients. Further analysis revealed decreased intrinsic/phasic alertness in TLE-FBTCS patients. Correlation analysis indicated that SCN topological properties were associated with alertness in TLE-FS patients but not in TLE-FBTCS patients. These findings suggest that TLE-FS and TLE-FBTCS patients show different changes in SCN integration and segregation, with TLE-FS alertness linked to SCN topological properties, providing insights into TLE's neuropathological mechanisms.

Selective disruption of gray matter volume covariance in orbitofrontal cortex subregions among patients with functional constipation

Functional constipation (FCon) is a prevalent common functional gastrointestinal disorders (FGIDs) frequently linked to mental and psychological disorders. Although previous studies have demonstrated alterations in brain structure and function in FCon, there remains a lack of investigation into the network-level structural inter-relationships (e.g., structural covariance) within key regions such as the orbitofrontal cortex (OFC). This study aimed to investigate whether gray matter volume (GMV) covariance in OFC subregions is selectively disrupted in FCon patients. A cohort of 87 patients with FCon and 87 healthy controls (HC) underwent high-resolution structural MRI scans. The GMV covariance was analyzed using voxel-based morphometry, and the covariance patterns between OFC subregions and other brain regions were examined using a general linear model. FCon patients demonstrated selective alterations in GMV covariance, notably within the lateral and medial OFC subregions, which showed altered covariance with brain regions associated with sensory, motor, and cognitive control functions, including the olfactory cortex, supplementary motor area, insula, and superior frontal gyrus. Our findings indicate that FCon patients show specific GMV covariance alterations in the OFC subregions, suggesting that these structural changes may be associated with disrupted brain-gut interactions and gastrointestinal dysfunction in patients with functional constipation, though the complex and bidirectional nature of gut-brain communication warrants further investigation.

Abnormal structural covariance network in major depressive disorder: Evidence from the REST-meta-MDD project

BACKGROUND

Major depressive disorder (MDD) is a common mental illness associated with brain morphological abnormalities. Although extensive studies have examined gray matter volume (GMV) changes in MDD, inconsistencies persist in reported findings. In the current study, we employed source-based morphometry (SBM) and structural covariance network (SCN) analyses to a large multi-center sample from the REST-meta-MDD database, aiming to characterize robust results of structural abnormalities in MDD.

METHODS

We analyzed 798 MDD patients and 974 healthy controls (HCs) from the REST-meta-MDD consortium. Voxel-based morphometry was applied to generate GMV maps. SBM was used to adaptively parcellate brain into different components, and SCN was constructed based on SBM components. Volume scores in each component and SCNs between the components were both compared between MDD and HC groups, as well as between first-episode drug-naive (FEDN) and recurrent MDD subgroups.

RESULTS

SBM identified 20 stable components. Three components encompassing the middle temporal gyrus, middle orbitofrontal gyrus and superior frontal gyrus exhibited volumetric differences between the MDD and HC groups. Volume differences were observed in the cingulate cortex and medial frontal gyrus between the FEDN and recurrent groups. SCN analysis revealed 9 aberrant pairs in MDD vs. HCs, and 7 pairs in FEDN vs. recurrent groups. All aberrant component pairs in the SCN implicated the prefrontal cortex.

CONCLUSIONS

These findings demonstrated brain structural deficits in MDD, and highlighted the prefrontal cortex as a central hub of SCN alterations. Our findings advance the understanding of MDD's neural mechanisms and suggest directions for diagnostic research.

[Illness duration-related developmental trajectory of progressive cerebral gray matter changes in schizophrenia]

In different stages of schizophrenia (SZ), alterations in gray matter volume (GMV) of patients are normally regulated by various pathological mechanisms. Instead of analyzing stage-specific changes, this study employed a multivariate structural covariance model and sliding-window approach to investigate the illness duration-related developmental trajectory of GMV in SZ. The trajectory is defined as a sequence of brain regions activated by illness duration, represented as a sparsely directed matrix. By applying this approach to structural magnetic resonance imaging data from 145 patients with SZ, we observed a continuous developmental trajectory of GMV from cortical to subcortical regions, with an average change occurring every 0.208 years, covering a time window of 20.176 years. The starting points were widely distributed across all networks, except for the ventral attention network. These findings provide insights into the neuropathological mechanism of SZ with a neuroprogressive model and facilitate the development of process for aided diagnosis and intervention with the starting points.

Changes of brain structure and structural covariance networks in Parkinson's disease with different sides of onset

Background

Parkinson's disease (PD) typically presents with unilateral symptoms in early stages, starting on one side and progressing, with the onset side showing more severe motor symptoms even after bilateralization. This asymmetry may reflect complex interactions among multiple brain regions and their network connections. In this study, we aimed to use surface-based morphometry (SBM) and structural covariance networks (SCNs) to investigate the differences in brain structure and network characteristics between patients with left-onset PD (LPD) and right-onset PD (RPD).

Methods

A total of 51 LPD and 49 RPD patients were recruited. Clinical assessments included the Unified Parkinson's Disease Rating Scale motor section, Hoehn and Yahr stage, Mini-Mental State Examination, Parkinson's Disease Questionnaire, and Beck Depression Inventory. All participants underwent 3 T structural MRI. FreeSurfer was used to perform vertex-wise comparisons of cortical surface area (CSA) and cortical thickness (CT), whereas the Brain Connectivity Toolbox was implemented to construct and analyze the structural covariance networks.

Results

In patients with LPD, we found reduced CSA in the right supramarginal gyrus (SMG), right precuneus (PCUN), left inferior parietal lobule (IPL), and left lingual gyrus (LING) compared to RPD, while no significant differences in CT were found between the two groups. The CSA of the right PCUN showed a significant positive correlation with MMSE score in LPD patients. In our SCNs analysis, LPD patients exhibited increased normalized characteristic path length and decreased small-world index in CSA-based networks, while in CT-based networks, they showed increased small-world index and global efficiency compared to RPD. No significant differences in nodal characteristics were observed in either CSA-based or CT-based networks between the two groups.

Conclusion

In patients with LPD, reductions in CSA observed in the right PCUN, right SMG, left IPL, and left LING may be associated with cognitive impairments and hallucinations among non-motor symptoms of PD. Additionally, the SCNs of LPD and RPD patients show significant differences in global topology, but regional node characteristics do not reflect lateralization differences. These findings offer new insights into the mechanisms of symptom lateralization in PD from the perspective of brain regional structure and network topology.

The analyses of structural covariance and structural covariance similarity of cortical morphological measures

Structural covariance refers to the concurrent changes in one morphological measure between two brain regions. Structural covariance of cortical morphological measures such as cortical thickness (CT), surface area (SA), and cortical volume (CV) have been applied to identify brain structural differences between patients with neuropsychiatric disorders and healthy controls. However, the precise relationships between structural covariance patterns of different cortical measures remain largely unknown. Here, we optimized the preprocessing and calculation approaches of structural covariances and investigated both global (whole-brain-level) and regional (brain-region-level) structural covariance similarities between CT, SA, and CV in 35,580 individuals. We found that Pearson correlation outperformed partial correlation due to generating fewer negative correlations of uncertain biological significance and principal component regression outperformed the regressions of total intracranial volume and respective global measures in removing global effects and reducing negative correlations. We observed that both global and regional covariance similarities of SA-CV were much higher than those of CT-CV and CT-SA, although they were influenced by the selection of atlases and covariance values. We also found age and sex effects on structural covariances and age effects on covariance similarities. The higher SA-CV covariance similarities than CT-CV indicates that SA contributes more to CV covariance than CT, although CV is derived from both CT and SA. The lack of CT-SA covariance similarities suggests that CT and SA have different covariance patterns and should be used in combination in structural covariance studies.

Using individualized structural covariance networks to analyze the heterogeneity of cerebral small vessel disease with depressive states

Objectives

Cerebral small vessel disease (CSVD) is a heterogeneous cerebrovascular syndrome with a variety of pathological mechanisms and clinical manifestations. A majority of research have shown that CSVD is associated with reduced expression of structural covariance networks (SCNs), but most of these SCN studies based on the group-level, which limits their ability to reflect individual variations in heterogeneous diseases. The purpose of this study is to analyze the structural covariance aberrations in patients with cerebral small vessels by utilizing individualized differential structural covariance network (IDSCN) analysis to explore the differences in SCNs and depressive states at the individual-level.

Methods

A total of 22 CSVD patients with depression (CSVD+D) and 34 healthy controls (HCs) were included in this study. IDSCNs were constructed for each subject based on regional gray matter volumes derived from their T1-weighted MRI images. The unpaired-sample t-test was used to compare the IDSCNs between the two groups to obtain the differential structural covariance edge and its distribution. Finally, correlation analyses were performed between the differential edge, the total CSVD imaging burden and Hamilton Rating Scale for Depression (HAMD) score.

Results

(1) Compared with HCs, the CSVD+D patients exhibited heterogeneous distributions of differential structural covariance edge, with the differential edge located between the caudate nucleus and the cerebellum. (2) There was a significant positive correlation between the total CSVD imaging burden and HAMD scores in CSVD patients with depression (r = 0.692, p < 0.001).

Conclusion

This study analyzed the IDSCNs between CSVD+D patients and HCs, which may indicate that the individual structural covariance aberrations between the caudate nucleus and cerebellum may contribute to depression in CSVD patients. Additionally, the higher total CSVD imaging burden of CSVD patients may indicate more severe depression. This finding suggests that early magnetic resonance imaging (MRI) assessment in CSVD patients may facilitate the early identification of depressive states and their severity in the near future.

Aberrant Structural-Functional Coupling of Large-Scale Brain Networks in Older Women With Subthreshold Depression

OBJECTIVES

Subthreshold depression (SD) is common in the older population, more so in females than males, and can lead to serious physical and mental ill-health. However, the underlying neurobiology remains unclear. This study used multimodal magnetic resonance imaging (MRI) to investigate the topological organization and coupling of the structural and functional brain networks in older women with SD.

METHODS

We constructed the structural network from diffusion MRI and the functional network from resting-state functional MRI in 50 older women with SD and 52 demographically matched older women healthy controls (HC). We used graph theory analysis to examine the topological properties of functional and structural networks, and structural-functional connectivity (SC-FC) coupling, and their potential relationship to depressive symptoms.

RESULTS

Globally, compared with older women HC, the older women with SD showed lower local efficiency in the structural network but not the functional network. Locally, older women with SD showed altered convergent nodal metrics in the default mode, salience, and sensorimotor network regions in both structural and functional networks. Moreover, SC-FC coupling reduced in older women with SD compared to older women HC. These network metric alterations were correlated with depressive symptoms.

DISCUSSION

Older women with SD showed alterations in both structural and functional networks, and in their coupling, which throw light on the role of large-scale brain networks in older female SD.

Radiomics feature similarity: A novel approach for characterizing brain network changes in patients with behavioral variant frontotemporal dementia

INTRODUCTION

Network modeling is increasingly used to study brain alterations in neurological disorders. In this study, we apply a novel modeling approach based on the similarity of regional radiomics feature to characterize gray matter network changes in patients with behavioral variant frontotemporal dementia (bvFTD) using MRI data.

METHODS

In this cross-sectional study, we assessed structural 3 T MRI data from twenty patients with bvFTD and 20 cognitively normal controls. Radiomics features were extracted from T1-weighted MRI based on cortical and subcortical brain segmentation. Similarity in radiomics features between brain regions was used to construct intra-individual structural gray matter networks. Regional mean connectivity strength (RMCS) and region-to-region radiomics similarity were compared between bvFTD patients and controls. Finally, associations between network measures, clinical data, and biological features were explored in bvFTD patients.

RESULTS

Relative to controls, patients with bvFTD showed higher RMCS values in the superior frontal gyrus, right inferior temporal gyrus and right inferior parietal gyrus (FDR-corrected p < 0.05). Patients with bvFTD also showed several edges of increased radiomics similarity in key components of the frontal, temporal, parietal and thalamic pathways compared to controls (FDR-corrected p < 0.05). Network measures in frontotemporal circuits were associated with Mini-Mental State Examination scores and cerebrospinal fluid total-tau protein levels (Spearman r > |0.7|, p < 0.005).

CONCLUSIONS

Our study provides new insights into frontotemporal network changes associated with bvFTD, highlighting specific associations between network measures and clinical/biological features. Radiomics feature similarity analysis could represent a useful approach for characterizing brain changes in patients with frontotemporal dementia.

Estimating Brain Similarity Networks with Diffusion MRI

Structural similarity has emerged as a promising tool in mapping the network organization of an individual, living human brain. Here, we propose diffusion similarity networks (DSNs), which employ rotationally invariant spherical harmonic features derived from diffusion magnetic resonance imaging (dMRI), to map gray matter structural organization. Compared to prior approaches, DSNs showed clearer laminar, cytoarchitectural, and micro-architectural organization; greater sensitivity to age, cognition, and sex; higher heritability in a large dataset of healthy young adults; and straightforward extension to non-cortical regions. We show DSNs are correlated with functional, structural, and gene expression connectomes and their gradients align with the sensory-fugal and sensorimotor-association axes of the cerebral cortex, including neuronal oscillatory dynamics, metabolism, immunity, and dopaminergic and glutaminergic receptor densities. DSNs can be easily integrated into conventional dMRI analysis, adding information complementary to structural white matter connectivity, and could prove useful in investigating a wide array of neurological and psychiatric conditions.

Multimodal evidence for cerebellar influence on cortical development in autism: structural growth amidst functional disruption

Despite perinatal damage to the cerebellum being one of the highest risk factors for later being diagnosed with autism spectrum disorder (ASD), it is not yet clear how the cerebellum might influence the development of cerebral cortex and whether this co-developmental process is distinct between neurotypical and ASD children. Leveraging a large structural brain MRI dataset of neurotypical children and those diagnosed with ASD, we examined whether structural variation in cerebellar tissue across individuals was correlated with neocortical variation during development, including the thalamus as a coupling factor. We found that the thalamus plays a distinct role in moderating cerebro-cerebellar structural coordination in ASD. Notably, structural coupling between cerebellum, thalamus, and neocortex was strongest in younger childhood and waned by early adolescence, mirroring a previously undescribed trajectory of behavioral development between ASD and neurotypical children. Complementary functional connectivity analyses likewise revealed atypical connectivity between cerebellum and neocortex in ASD. This relationship was particularly prominent in a model of cerebellar structure predicting functional connectivity, where ASD and neurotypical children showed divergent patterns. Interestingly, these functional-structural relationships became more prominent with age, while structural effects were most prominent earlier in childhood, and showed significant lateralization. This pattern may suggest a developmental sequence where early uncoordinated structural growth amongst regions is followed by increasingly atypical functional synchronization. These findings provide multimodal evidence in the living brain for a cerebellar diaschisis model of autism, where both increased cerebellar-cerebral structural coupling and altered functional connectivity in cerebro-cerebellar pathways contribute to the ontogeny of this neurodevelopmental disorder.

Fusion of brain imaging genetic data for alzheimer's disease diagnosis and causal factors identification using multi-stream attention mechanisms and graph convolutional networks

Correctly diagnosing Alzheimer's disease (AD) and identifying pathogenic brain regions and genes play a vital role in understanding the AD and developing effective prevention and treatment strategies. Recent works combine imaging and genetic data, and leverage the strengths of both modalities to achieve better classification results. In this work, we propose MCA-GCN, a Multi-stream Cross-Attention and Graph Convolutional Network-based classification method for AD patients. It first constructs a brain region-gene association network based on brain region fMRI time series and gene SNP data. Then it integrates the absolute and relative positions of the brain region time series to obtain a new brain region time series containing temporal information. Then long-range and local association features between brain regions and genes are sequentially aggregated by multi-stream cross-attention and graph convolutional networks. Finally, the learned brain region and gene features are input to the fully connected network to predict AD types. Experimental results on the ADNI dataset show that our model outperforms other methods in AD classification tasks. Moreover, MCA-GCN designed a multi-stage feature scoring process to extract high-risk genes and brain regions related to disease classification.

Resolving heterogeneity of early-onset major depressive disorder through individual differential structural covariance network analysis

BACKGROUND

Early-onset major depressive disorder (EO-MDD) is characterized by its significant heterogeneity, hindering progress in research. Traditional case-control studies, like group-level structural covariance network, struggle to capture individual heterogeneity among EO-MDD patients.

METHODS

In this study, T1-weighted structural magnetic resonance imaging was obtained from 185 participants, including 103 EO-MDD patients and 82 healthy controls. A subject-level individual differential structural covariance network (IDSCN) was constructed for each patient based on the concept of normative model. Semi-supervised clustering algorithms were then employed to classify EO-MDD subtypes, followed by validation analyses to assess clustering stability.

RESULTS

Our study identified two neuroanatomical subtypes. The low-covariance subtype is characterized by significant neural maturation gaps across the whole brain and more pronounced anxiety somatization symptoms. Conversely, the high-covariance subtype demonstrates simultaneous mature of brain structures.

CONCLUSION

Our findings provide valuable insights into the neuroanatomical heterogeneity of EO-MDD patients, highlighting the importance of considering individual symptom profiles in subtype classification. These findings have substantial clinical implications for personalized treatment and precision medicine, offering more effective treatment choices and accurate diagnoses.

Deep structural brain imaging via computational three-photon microscopy

Significance

High-resolution optical imaging at significant depths is challenging due to scattering, which impairs image quality in living matter with complex structures. We address the need for improved imaging techniques in deep tissues.

Aim

We aim to develop a computational deep three-photon microscopy (3PM) method that enhances image quality without compromising acquisition speed, increasing excitation power, or adding extra optical components.

Approach

We introduce a method called low-rank diffusion model (LRDM)-3PM, which utilizes customized aggregation-induced emission nanoprobes and self-supervised deep learning. This approach leverages superficial information from three-dimensional (3D) images to compensate for scattering and structured noise from the imaging system.

Results

LRDM-3PM achieves a remarkable signal-to-background ratio above 100 even at depths of 1.5 mm, enabling the imaging of the hippocampus in live mouse brains. It integrates with a multiparametric analysis platform for resolving morpho-structural features of brain vasculature in a completely 3D manner, accurately recognizing distinct brain regions.

Conclusions

LRDM-3PM demonstrates the potential for minimally invasive in vivo imaging and analysis, offering a significant advancement in the field of deep tissue imaging by maintaining high-resolution quality at unprecedented depths.

Cortical Morphometric Similarity Remodeling in Traumatic Brain Injury Links Cognitive Impairments with Transcriptional Changes and Type-Specific Cells

The heterogeneous injuries and resulting cognitive deficits pose significant challenges in the clinical management of mild traumatic brain injury (mTBI). However, the pathophysiological mechanisms related to heterogeneities of mTBI are still unclear. This study aims to explore the mechanisms underlying brain remodeling by examining the morphometric similarity (MS) alterations and corresponding transcriptomic signatures across adult and pediatric mTBI (adult mTBI: 112 acute patients, 47 follow-up chronic patients, 66 healthy controls [HCs]; pediatric mTBI: 30 acute patients, 31 HCs). A healthy adult cohort (N = 840) is included to derive the modularized brain MS networks representing interregional cortical connectivity. Subsequently, cortical MS remodeling patterns are identified involving mostly MS increases in the frontal modules with typical high MS and decreases in the occipital module with typical low MS, with more pronounced changes observed in the developing brain with mTBI. The abnormal MS changes are correlated with variable cognitive impairments. Moreover, cortical MS remodeling is also associated with the genes enriched in CA1 pyramidal cells and neuron-specific biological processes. The transcription-related cortical remodeling in mTBI might reveal the disruption of brain cellular architecture. Therapeutic modalities to intervene in specific cortex and tackle CA1 over-activation might better encircle the neurobiology of TBI.

Early Development and Co-Evolution of Microstructural and Functional Brain Connectomes: A Multi-Modal MRI Study in Preterm and Full-Term Infants

Functional networks characterized by coherent neural activity across distributed brain regions have been observed to emerge early in neurodevelopment. Synchronized maturation across regions that relate to functional connectivity (FC) could be partially reflected in the developmental changes in underlying microstructure. Nevertheless, covariation of regional microstructural properties, termed "microstructural connectivity" (MC), and its relationship to the emergence of functional specialization during the early neurodevelopmental period remain poorly understood. We investigated the evolution of MC and FC postnatally across a set of cortical and subcortical regions, focusing on 45 preterm infants scanned longitudinally, and compared to 45 matched full-term neonates as part of the developing Human Connectome Project (dHCP) using direct comparisons of grey-matter connectivity strengths as well as network-based analyses. Our findings revealed a global strengthening of both MC and FC with age, with connection-specific variability influenced by the connection maturational stage. Prematurity at term-equivalent age was associated with significant connectivity disruptions, particularly in FC. During the preterm period, direct comparisons of MC and FC strength showed a positive linear relationship, which seemed to weaken with development. On the other hand, overlaps between MC- and FC-derived networks (estimated with Mutual Information) increased with age, suggesting a potential convergence towards a shared underlying network structure that may support the co-evolution of microstructural and functional systems. Our study offers novel insights into the dynamic interplay between microstructural and functional brain development and highlights the potential of MC as a complementary descriptor for characterizing brain network development and alterations due to perinatal insults such as premature birth.

The structural covariance of reading-related brain regions in adults and children with typical or poor reading skills

Structural covariance (SC) is a promising approach for studying brain organization in the context of literacy and developmental disorders, offering insights into both structural and functional underpinnings and potential experience-dependent co-development of functional brain networks. Here, we explore the influence of maturation and reading skill on SC in reading-related brain regions. Whole-brain SC analyses were conducted for six key regions of the reading network, including an anterior and posterior subdivision of the visual word form area (VWFA). To study maturational effects, SC was compared between typical-reading adults (N = 134, 25.3 ± 4 yrs) and children (N = 110, 9.6 ± 1.6 yrs). The impact of reading skills on SC was assessed by comparing typical-reading children (N = 110, 9.6 ± 1.6 yrs) to children with poor reading skills (N = 68, 10.2 ± 1.4 yrs). Our results showed significant SC between reading-related brain regions in typical-reading adults. Further, we observed significant SC between the posterior VWFA and the occipital cortex, and between the anterior VWFA and the superior temporal and inferior frontal gyri. There was no indication of a major change in SC within the reading network related to maturation. However, we observed higher SC between the inferior parietal lobule and other reading-related brain regions in children with typical compared to poor reading skills.

Local Structural Indices Changes During Different Periods of Postherpetic Neuralgia: A Graphical Study in Structural Covariance Networks

Purpose

In this study, we aim to explore the changes in network graph theory indices of structural covariance networks (SCNs) in PHN patients with different disease durations.

Patients and Methods

High-resolution T1 magnetic resonance images were collected from 109 subjects. We constructed SCNs based on cortical thickness data and analyzed the changes in global and regional network measures of PHN patients and herpes zoster (HZ) patients, and get hubs of each group.

Results

(1) PHN patients with a disease duration >6 months had reduced global efficiency (P=0.035) and increased characteristic shortest path length (P=0.028). (2) Nodal efficiency of the right pars opercularis was greater in both HZ and PHN patients with a disease duration of 1 to 3 months (P<0.001); in PHN patients with a disease duration > 6 months, the nodal degree of the left pars triangularis and nodal efficiency of the right middle temporal gyrus were greater (P<0.001). (3) The right supramarginal gyrus was the common hub of healthy controls (HCs) and HZ patients, the right pars opercularis was the common hub of HZ patients and PHN patients with a disease duration of 1 to 3 months, and the bilateral superior frontal gyrus was the common hub of HZ patients and PHN patients with a disease duration >6 months.

Conclusion

There have changes in SCN indices in PHN patients with different disease durations. PHN patients with a disease duration >6 months had increased SCN integration and diminished information transfer capability between nodes, which complemented the topological properties of previous PHN networks. Eglobal and Lp can be considered as potential imaging markers for future clinical restaging of PHN.

Masked Deformation Modeling for Volumetric Brain MRI Self-Supervised Pre-Training

Self-supervised learning (SSL) has been proposed to alleviate neural networks' reliance on annotated data and to improve downstream tasks' performance, which has obtained substantial success in several volumetric medical image segmentation tasks. However, most existing approaches are designed and pre-trained on CT or MRI datasets of non-brain organs. The lack of brain prior limits those methods' performance on brain segmentation, especially on fine-grained brain parcellation. To overcome this limitation, we here propose a novel SSL strategy for MRI of the human brain, named Masked Deformation Modeling (MDM). MDM first conducts atlas-guided patch sampling on individual brain MRI scans (moving volumes) and an MNI152 template (a fixed volume). The sampled moving volumes are randomly masked in a feature-aligned manner, and then sent into a U-Net-based network to extract latent features. An intensity head and a deformation field head are used to decode the latent features, respectively restoring the masked volume and predicting the deformation field from the moving volume to the fixed volume. The proposed MDM is fine-tuned and evaluated on three brain parcellation datasets with different granularities (JHU, Mindboggle-101, CANDI), a brain lesion segmentation dataset (ATLAS2), and a brain tumor segmentation dataset (BraTS21). Results demonstrate that MDM outperforms various state-of-the-art medical SSL methods by considerable margins, and can effectively reduce the annotation effort by at least 40%. Codes and pre-trained weights will be released at https://github.com/CRazorback/MDM.

Structural covariance network patterns linked to neuropsychiatric symptoms in biologically defined Alzheimer's disease: Insights from the mild behavioral impairment checklist

BACKGROUND

The frequent presentation of Alzheimer's disease (AD) with neuropsychiatric symptoms (NPS) in the context of normal or minimally-impaired cognitive function led to the concept of Mild Behavioral Impairment (MBI). While MBI's impact on subsequent cognitive decline is recognized, its association with brain network changes in biologically-defined AD remains unexplored.

OBJECTIVE

To investigate the correlation of structural covariance networks with MBI-C checklist sub-scores in biologically-defined AD patients.

METHODS

We analyzed 33 biologically-defined AD patients, ranging from mild cognitive impairment to early dementia, all characterized as amyloid-positive through cerebrospinal fluid analysis or amyloid positron emission tomography scans. Regional network properties were assessed through graph theory.

RESULTS

Affective dysregulation correlated with decreased segregation and integration in the right inferior frontal gyrus (IFG). Impulse dyscontrol and social inappropriateness correlated positively with centrality and efficiency in the right posterior cingulate cortex (PCC). Global network properties showed a preserved small-world organization.

CONCLUSIONS

This study reveals associations between MBI subdomains and structural brain network alterations in biologically-confirmed AD. The IFG's involvement is crucial for mood dysregulation, while the PCC could be involved in compensatory mechanisms for social cognition and impulse control. These findings underscore the significance of biomarker-based neuroimaging for the characterization of NPS across the AD spectrum.

Developmental Mismatch Across Brain Modalities in Young Children

Background: Brain development during the preschool period is complex and extensive and underlies ongoing behavioral and cognitive maturation. Increasing understanding of typical brain maturation during this time is critical to early identification of atypical development and could inform treatments and interventions. Previous studies have suggested mismatches between brain structural and functional development in later childhood and adolescence. The current study aimed to delineate the developmental matches and mismatches between brain measures from multiple magnetic resonance imaging modalities in young children. Methods: Brain volume, cortical thickness, fractional anisotropy, cerebral blood flow (CBF), amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), and eigenvector centrality mapping (ECM) were included. Multi-modal neuroimages for 159 datasets from 67 typically developing preschoolers (2.0-7.6 years old) were collected and analyzed. Results: Functional measures (CBF, ECM, ReHo, ALFF) had similar developmental trajectories across regions, whereas development trajectories for brain volumes and cortical thickness were more heterogeneous. Furthermore, within individuals, brain volumes and cortical thickness were very good at predicting individual scans from prior longitudinal scans. Conclusions: These findings provide a more detailed characterization of the complex interplay of different types of brain development in the early years, laying the foundation for future studies on the impact of environmental factors and neurodevelopmental disorders on the development matches/mismatches patterns between brain areas and modalities.

Altered individual-based morphological brain network in type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our study, we enrolled 123 patients with T2DM and 91 healthy controls (HC). We constructed the networks using symmetric Kullback-Leibler (KL) divergence-based similarity (KLS) and calculated various global and nodal metrics based on graph theory to describe the topological properties of the networks. Firstly, T2DM exhibited increased nodal degree in the left para-hippocampus, left amygdala, left precuneus, bilateral putamen, and right inferior temporal gyrus, and the concentrations of glycosylated hemoglobin (HbA1c) were positively correlated with the nodal degree of the left precuneus. Secondly, we identified hypo-connected and hyper-connected subnetworks, primarily involved with reward circuits and attention network, respectively. Lastly, altered morphological connectivity (MC) was linked to cognitive performance, and the aforementioned subnetworks may serve as predictors of cognitive performance. In conclusion, this study provided neuroimaging evidence for understanding cognitive changes by analyzing the properties and connections of individual-based morphological brain networks (MBNs) in T2DM patients.

Structural Cerebellar and Lateral Frontoparietal Networks are altered in CUD: An SBM Analysis

Repetitive drug use results in enduring structural and functional changes in the brain. Addiction research has consistently revealed significant modifications in key brain networks related to reward, habit, salience, executive function, memory and self-regulation. Techniques like Voxel-based Morphometry have highlighted large-scale structural differences in grey matter across distinct groups. Source-based Morphometry (SBM) takes this a step further by incorporating the Independent Component Analysis to detect shared patterns of grey matter variation, all without requiring prior selection of regions of interest. However, SBM has yet to be employed in the study of structural alteration patterns related to cocaine addiction. Therefore, we performed this analysis to explore alterations in structural covariance specific to cocaine addiction. Our study involved 40 individuals diagnosed with Cocaine Use Disorder (CUD) and 40 matched healthy controls. Participants with CUD completed clinical questionnaires assessing the severity of their dependence and other relevant clinical variables. Following the adjustment for age-related effects, we observed notable disparities between groups in two structural independent components, which we identified as the structural cerebellar network and the structural lateral frontoparietal network, which display opposing trends. Specifically, the individuals with CUD exhibited a heightened contribution to the cerebellar network but simultaneously demonstrated a reduced contribution to the lateral frontoparietal network compared to the healthy controls. These findings unveil distinctive covariance patterns of neuroregulation linked with cocaine addiction, which indicates an interruption in the typical structural development in an affected lateral frontoparietal network, while suggesting an extended pattern of neuroregulation within the cerebellar network in individuals with CUD.

Comparing Intra- and Inter-individual Correlational Brain Connectivity from Functional and Structural Neuroimaging Data

Inferring brain connectivity from inter-individual correlations has been applied across various neuroimaging modalities, including positron emission tomography (PET) and MRI. The variability underlying these inter-individual correlations is generally attributed to factors such as genetics, life experiences, and long-term influences like aging. This study leveraged two unique longitudinal datasets to examine intra-individual correlations of structural and functional brain measures across an extended time span. By focusing on intra-individual correlations, we aimed to minimize individual differences and investigate how aging and state-like effects contribute to brain connectivity patterns. Additionally, we compared intra-individual correlations with inter-individual correlations to better understand their relationship. In the first dataset, which included repeated scans from a single individual over 15 years, we found that intra-individual correlations in both regional homogeneity (ReHo) during resting-state and gray matter volumes (GMV) from structural MRI closely resembled resting-state functional connectivity. However, ReHo correlations were primarily driven by state-like variability, whereas GMV correlations were mainly influenced by aging. The second dataset, comprising multiple participants with longitudinal Fludeoxyglucose (18F) FDG-PET and MRI scans, replicated these findings. Both intra- and inter-individual correlations were strongly associated with resting-state functional connectivity, with functional measures (i.e., ReHo and FDG-PET) exhibiting greater similarity to resting-state connectivity than structural measures. This study demonstrated that controlling for various factors can enhance the interpretability of brain correlation structures. While inter- and intra-individual correlation patterns showed similarities, accounting for additional variables may improve our understanding of inter-individual connectivity measures.

Examining neuroanatomical correlates of win-stay, lose-shift behaviour

This study aimed to better understand the neuroanatomical correlates of decision-making strategies, particularly focusing on win-stay and lose-shift behaviours, using voxel-based morphometry (VBM) in a large cohort of healthy adults. Participants completed a forced-choice card-guessing task designed to elicit behavioural responses to rewards and losses. Using this task, we investigated the relationship between win-stay and lose-shift behaviour and both grey matter volume (GMV) and white matter volume (WMV). The frequency of win-stay and lose-shift behaviours was calculated for each participant and entered into VBM analyses alongside GMV and WMV measures. Our results revealed that increased lose-shift behaviour was associated with reduced GMV in key brain regions, comprising of the left superior temporal gyrus, right middle temporal gyrus, and the bilateral superior lateral occipital cortices. Interestingly, no significant associations were found between GMV or WMV, and win-stay behaviour. These results suggest that specific regions within the temporal and occipital lobes may be involved in modulating decision-making strategies following negative outcomes. Further analyses revealed that increased lose-shift behaviour was also associated with increased WMV in the left superior temporal gyrus. The absence of significant findings in relation to win-stay behaviour and the differential involvement of brain structures in lose-shift responses indicate that decision-making in the face of losses may involve distinct neuroanatomical mechanisms compared to decision-making following wins. This study advances our understanding of the structural brain correlates linked to decision-making strategies and highlights the complexity of brain-behaviour relationships in choice behaviour.

Disrupted intersubject variability architecture in structural and functional brain connectomes in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a heterogeneous condition characterized by significant intersubject variability in clinical presentations. Recent neuroimaging studies have indicated that MDD involves altered brain connectivity across widespread regions. However, the variability in abnormal connectivity among MDD patients remains understudied.

METHODS

Utilizing a large, multi-site dataset comprising 1,276 patients with MDD and 1,104 matched healthy controls, this study aimed to investigate the intersubject variability of structural covariance (IVSC) and functional connectivity (IVFC) in MDD.

RESULTS

Patients with MDD demonstrated higher IVSC in the precuneus and lingual gyrus, but lower IVSC in the medial frontal gyrus, calcarine, cuneus, and cerebellum anterior lobe. Conversely, they exhibited an overall increase in IVFC across almost the entire brain, including the middle frontal gyrus, anterior cingulate cortex, hippocampus, insula, striatum, and precuneus. Correlation and mediation analyses revealed that abnormal IVSC was positively correlated with gray matter atrophy and mediated the relationship between abnormal IVFC and gray matter atrophy. As the disease progressed, IVFC increased in the left striatum, insula, right lingual gyrus, posterior cingulate, and left calcarine. Pharmacotherapy significantly reduced IVFC in the right insula, superior temporal gyrus, and inferior parietal lobule. Furthermore, we found significant but distinct correlations between abnormal IVSC and IVFC and the distribution of neurotransmitter receptors, suggesting potential molecular underpinnings. Further analysis confirmed that abnormal patterns of IVSC and IVFC were reproducible and MDD specificity.

CONCLUSIONS

These results elucidate the heterogeneity of abnormal connectivity in MDD, underscoring the importance of addressing this heterogeneity in future research.

Neuroimaging advances in neurocognitive disorders among HIV-infected individuals

Although combination antiretroviral therapy (cART) has been widely applied and effectively extends the lifespan of patients infected with human immunodeficiency virus (HIV), these patients remain at a substantially increased risk of developing neurocognitive impairment, commonly referred to as HIV-associated neurocognitive disorders (HAND). Magnetic resonance imaging (MRI) has emerged as an indispensable tool for characterizing the brain function and structure. In this review, we focus on the applications of various MRI-based neuroimaging techniques in individuals infected with HIV. Functional MRI, structural MRI, diffusion MRI, and quantitative MRI have all contributed to advancing our comprehension of the neurological alterations caused by HIV. It is hoped that more reliable evidence can be achieved to fully determine the driving factors of cognitive impairment in HIV through the combination of multi-modal MRI and the utilization of more advanced neuroimaging analysis methods.

A multimodal characterization of low-dimensional thalamocortical structural connectivity patterns

The human thalamus is a heterogeneous subcortical structure coordinating whole-brain activity. Investigations of its internal organization reveal differentiable subnuclei, however, a consensus on subnuclei boundaries remains absent. Recent work suggests that thalamic organization additionally reflects continuous axes transcending nuclear boundaries. Here, we study how low-dimensional axes of thalamocortical structural connectivity relate to intrathalamic microstructural features, functional connectivity, and structural covariance. Using diffusion MRI, we compute a thalamocortical structural connectome and derive two main axes of thalamic organization. The principal axis, extending from medial to lateral, relates to intrathalamic myelin, and functional connectivity organization. The secondary axis corresponds to the core-matrix cell distribution. Lastly, exploring multimodal associations globally, we observe the principal axis consistently differentiating limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varies. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.

Large-scale structural covariance networks changes relate to executive function deficit in betel quid-dependent chewers

Previous studies demonstrate deficits in executive function for betel quid-dependent (BQD) patients. Large-scale structural covariance network (SCN) based on gray matter (GM) morphometry may be able to explore the neural mechanism of executive dysfunction in BQD individuals. This study aims to identify spatial covariance patterns of GM volume and to investigate their association with executive dysfunction in BQD individuals. Sixty-four BQD individuals and 48 sex- and age-matched healthy controls (HCs) underwent T1-weighted structural MRI examination and executive function assessments, including the Backward Digit Span (BDS) test and Stroop Color and Word (SCW) test. Seventy SCNs based on GM volume covariance patterns were defined using independent component analysis. An SCN-based classifier was constructed to differentiate between BQD and HC individuals. Receiver operating characteristic (ROC) curves were applied to evaluate the performance of the SCN-based classifier. Linear regression analyses were performed to investigate the association between SCN network indices and executive function indices. Six SCNs had higher classifications for differentiating between BQD and HC individuals. The area under the ROC curve of the SCN-based classifier was 0.812 in the training set and 0.771 in the testing set. Furthermore, linear regression analyses demonstrated that the network indices in the thalamus were associated with BDS scores adjusted for age, sex, and education. Large-scale SCNs could provide potential imaging markers for differentiating BQD and HC groups. The loss of network index in the thalamus was associated with working memory, indicating that SCNs could reveal executive dysfunction in BQD individuals.

Individualized brain radiomics-based network tracks distinct subtypes and abnormal patterns in prodromal Parkinson's disease

Individuals in the prodromal phase of Parkinson's disease (PD) exhibit significant heterogeneity and can be divided into distinct subtypes based on clinical symptoms, pathological mechanisms, and brain network patterns. However, little has been done regarding the valid subtyping of prodromal PD, which hinders the early diagnosis of PD. Therefore, we aimed to identify the subtypes of prodromal PD using the brain radiomics-based network and examine the unique patterns linked to the clinical presentations of each subtype. Individualized brain radiomics-based network was constructed for normal controls (NC; N = 110), prodromal PD patients (N = 262), and PD patients (N = 108). A data-driven clustering approach using the radiomics-based network was carried out to cluster prodromal PD patients into higher-/lower-risk subtypes. Then, the dissociated patterns of clinical manifestations, anatomical structure alterations, and gene expression between these two subtypes were evaluated. Clustering findings indicated that one prodromal PD subtype closely resembled the pattern of NCs (N-P; N = 159), while the other was similar to the pattern of PD (P-P; N = 103). Significant differences were observed between the subtypes in terms of multiple clinical measurements, neuroimaging for morphological changes, and gene enrichment for synaptic transmission. Identification of prodromal PD subtypes based on brain connectomes and a full understanding of heterogeneity at this phase could inform early and accurate PD diagnosis and effective neuroprotective interventions.

Individualized gray matter morphological abnormalities unveil two neuroanatomical obsessive-compulsive disorder subtypes

Obsessive-compulsive disorder (OCD) is a highly heterogeneous disorder, with notable variations among cases in structural brain abnormalities. To address this heterogeneity, our study aimed to delineate OCD subtypes based on individualized gray matter morphological differences. We recruited 100 untreated, first-episode OCD patients and 106 healthy controls for structural imaging scans. Utilizing normative models of gray matter volume, we identified subtypes based on individual morphological abnormalities. Sensitivity analyses were conducted to validate the reproducibility of clustering outcomes. To gain deeper insights into the connectomic and molecular underpinnings of structural brain abnormalities in the identified subtypes, we investigated their associations with normal brain network architecture and the distribution of neurotransmitter receptors/transporters. Our findings revealed two distinct OCD subtypes exhibiting divergent patterns of structural brain abnormalities. Sensitivity analysis results confirmed the robustness of the identified subtypes. Subtype 1 displayed significantly increased gray matter volume in regions including the frontal gyrus, precuneus, insula, hippocampus, parahippocampal gyrus, amygdala, and temporal gyrus, while subtype 2 exhibited decreased gray matter volume in the frontal gyrus, precuneus, insula, superior parietal gyrus, temporal gyrus, and fusiform gyrus. When considering all patients collectively, structural brain abnormalities nullified. The identified subtypes were characterized by divergent disease epicenters. Specifically, subtype 1 showed disease epicenters in the middle frontal gyrus, while subtype 2 displayed disease epicenters in the striatum, thalamus and hippocampus. Furthermore, structural brain abnormalities in these subtypes displayed distinct associations with neurotransmitter receptors/transporters. The identified subtypes offer novel insights into nosology and the heterogeneous nature of OCD.

Reorganization of gray matter networks in patients with Moyamoya disease

Patients with Moyamoya disease (MMD) exhibit significant alterations in brain structure and function, but knowledge regarding gray matter networks is limited. The study enrolled 136 MMD patients and 99 healthy controls (HCs). Clinical characteristics and gray matter network topology were analyzed. Compared to HCs, MMD patients exhibited decreased clustering coefficient (Cp) (P = 0.006) and local efficiency (Eloc) (P = 0.013). Ischemic patients showed decreased Eloc and increased characteristic path length (Lp) compared to asymptomatic and hemorrhagic patients (P < 0.001, Bonferroni corrected). MMD patients had significant regional abnormalities, including decreased degree centrality (DC) in the left medial orbital superior frontal gyrus, left orbital inferior frontal gyrus, and right calcarine fissure and surrounding cortex (P < 0.05, FDR corrected). Increased DC was found in bilateral olfactory regions, with higher betweenness centrality (BC) in the right median cingulate, paracingulate fusiform gyrus, and left pallidum (P < 0.05, FDR corrected). Ischemic patients had lower BC in the right hippocampus compared to hemorrhagic patients, while hemorrhagic patients had decreased DC in the right triangular part of the inferior frontal gyrus compared to asymptomatic patients (P < 0.05, Bonferroni corrected). Subnetworks related to MMD and white matter hyperintensity volume were identified. There is significant reorganization of gray matter networks in patients compared to HCs, and among different types of patients. Gray matter networks can effectively detect MMD-related brain structural changes.

Explaining cognitive function in multiple sclerosis through networks of grey and white matter features: a joint independent component analysis

Cognitive impairment (CI) in multiple sclerosis (MS) is only partially explained by whole-brain volume measures, but independent component analysis (ICA) can extract regional patterns of damage in grey matter (GM) or white matter (WM) that have proven more closely associated with CI. Pathology in GM and WM occurs in parallel, and so patterns can span both. This study assessed whether joint-ICA of GM and WM features better explained cognitive function compared to single-tissue ICA. 89 people with MS underwent cognitive testing and magnetic resonance imaging. Structural T1 and diffusion-weighted images were used to measure GM volumes and WM connectomes (based on fractional anisotropy weighted by the number of streamlines). ICA was performed for each tissue type separately and as joint-ICA. For each tissue type and joint-ICA, 20 components were extracted. In stepwise linear regression models, joint-ICA components were significantly associated with all cognitive domains. Joint-ICA showed the highest variance explained for executive function (Adjusted R2 = 0.35) and visual memory (Adjusted R2 = 0.30), while WM-ICA explained the highest variance for working memory (Adjusted R2 = 0.23). No significant differences were found between joint-ICA and single-tissue ICA in information processing speed or verbal memory. This is the first MS study to explore GM and WM features in a joint-ICA approach and shows that joint-ICA outperforms single-tissue analysis in some, but not all cognitive domains. This highlights that cognitive domains are differentially affected by tissue-specific features in MS and that processes spanning GM and WM should be considered when explaining cognition.

Reorganization of cortical individualized differential structural covariance network is associated with regional morphometric changes in chronic subcortical stroke

Patients with chronic subcortical stroke undergo regional and network morphometric reorganizations beyond the lesion site, but the interplay between network and regional reorganization remains poorly understood. We aimed to clarify the reorganization patterns of the individualized differential structural covariance networks (IDSCN) in chronic subcortical stroke and investigate their associations with regional gray matter volume (GMV) changes and functional recovery. Structural MRI from four datasets enrolled 112 patients with chronic subcortical stroke (81 male, age: 55.82 ± 7.79) and 122 matched healthy controls (HC) (74 male; age: 55.28 ± 7.54). Network-based statistics were employed to identify aberrant IDSCN, Spearman correlation was conducted to assess the association between IDSCN and regional GMV alterations, and partial correlation was utilized to investigate the association between abnormal IDSCN and functional recovery. We identified 133 connections with balanced increased and decreased IDSCN. Aberrant IDSCN involved more regions than local GMV alterations, local GMV alteration exhibited intricate correlations with IDSCN, which could explain partly IDSCN reorganization (p < 0.05, corrected). Finally, abnormal IDSCN showed a weak association with long-term clinical recovery (p < 0.01). These findings reinforce the theory of adaptive network reorganization post-stroke and suggest that IDSCN may provide further insights into cortical reorganization and functional rehabilitation beyond regional morphometric measures.

Increased structural covariance of cortical measures in individuals with an at-risk mental state

An anomalous pattern of structural covariance has been reported in schizophrenia, which has been suggested to represent connectome changes during brain maturation and neuroprogressive processes. It remains unclear whether similar differences exist in a clinical high-risk state for psychosis, and if they are associated with a prodromal phenotype and/or later psychosis onset. This multicenter magnetic resonance imaging study cross-sectionally examined structural covariance in a large at-risk mental state (ARMS) sample with different outcomes. The whole-brain structural covariance of four cortical measures (thickness, area, volume, and gyrification) was assessed in 155 individuals with ARMS, who were subclassified into 26 (16.8 %) with a later psychosis onset (ARMS-P), 44 with persistent subthreshold psychotic symptoms, and 53 with the remission of psychotic symptoms (ARMS-R) during the clinical follow-up, and 191 healthy controls. The relationships of changes in structural covariance with clinical symptoms and cognitive impairments were also investigated in the ARMS subsample. Structural covariance was significantly higher in widespread cortical regions in the ARMS group than in the controls, with each cortical measure having a different pattern in affected cortical regions. The higher structural covariance of the cortical area was partly related to severe suspiciousness-persecutory ideation. Structural covariance was significantly higher, mainly in fronto-parietal gyrification, in the ARMS-P group than in the ARMS-R group. The present results suggest that changes in structural covariance result in psychosis vulnerability and the excessive structural covariance of brain gyrification in ARMS subjects may contribute to their later clinical course.

Graph analysis based on SCN reveals novel neuroanatomical targets related to tinnitus distress

Purpose

Tinnitus is considered a neurological disorder affecting both auditory and nonauditory networks. This study aimed to investigate the structural brain covariance network in tinnitus patients and analyze its altered topological properties.

Materials

Fifty three primary tinnitus patients and 67 age- and sex-matched healthy controls (HCs) were included. Gray matter volume (GMV) of each participant was extracted using voxel-based morphometry, a group-level structural covariance network (SCN) was constructed based on the GMV of each participant, and graph theoretic analyses were performed using graph analysis toolbox (GAT). The differences in the topological properties of SCN between both groups were compared and analyzed.

Results

Both groups exhibited small-world attributes. Compared with HCs, tinnitus patients had significantly higher characteristic path length, lambda, transitivity, and assortativity (p < 0.05), and significantly lower global efficiency (p < 0.05). Tinnitus patients had higher clustering coefficient and reduced gamma and modularity, but neither was remarkable. The hubs in tinnitus network focused on the temporal lobe. In addition, the tinnitus network was found to be reduced in robustness to targeted attacks compared with HCs. Besides, a significant negative correlation between Tinnitus Handicap Inventory (THI) score and GMV in the left angular gyrus (r = -0.283, p = 0.040) as well as left superior temporal pole (r = -0.282, p = 0.041) were identified.

Conclusion

Tinnitus patients showed reduced small-world properties, altered hub nodes, and reduced ability to respond to targeted attacks in brain network. The GMV in the left angular gyrus and left superior temporal pole showed significant negative correlation with tinnitus distress (THI score), indicating potential therapeutic target.

Graph Theoretical Measures for Alzheimer's, MCI, and Normal Controls: A Comparative Study Using MRI Data

Background

The Graph theory provides the platform that could be used to model complex brain networks mathematically, and it could play a significant role in the diagnosis of various neurodegenerative diseases such as Alzheimer's.

Purpose

The main aim of our study is to perform a comparative analysis in terms of various graph theoretic measures of structural brain networks. In particular, the paper evaluates graph theoretical measures by first forming graphs using magnetic resonance imaging (MRI) data.

Method

In this paper, we study and evaluate graph theoretical measures using MRI data, namely characteristic path length, global efficiency, strength, and clustering coefficient, in a cohort of normal controls (N = 30), a cohort of mild cognitive impairment (MCI) (N = 30), and a cohort of Alzheimer's disease (AD) (N = 30). In our work, MRI data is preprocessed and cortical thickness is extracted for each brain region. The connectivity matrix is obtained, and thus a graph is formed. We have also performed receiver operating characteristic (ROC) and area under the ROC analyses of all graph theoretical measures to better elucidate and validate the results.

Results

It is observed that these measures may be used to differentiate Alzheimer's from normal. In our study, we observed that a very random and disrupted network is obtained in the case of Alzheimer's in comparison with the normal and MCI cases. The other observations in terms of graph theoretic measures are an increase in characteristic path length, a decrease in global efficiency, a decrease in strength, and a reduction in values of the clustering coefficient in the case of Alzheimer's.

Conclusion

The findings suggest that graph theoretical measures and alterations in network topology could be used as quantitative biomarkers of AD.

Structural MRI of brain similarity networks

Recent advances in structural MRI analytics now allow the network organization of individual brains to be comprehensively mapped through the use of the biologically principled metric of anatomical similarity. In this Review, we offer an overview of the measurement and meaning of structural MRI similarity, especially in relation to two key assumptions that often underlie its interpretation: (i) that MRI similarity can be representative of architectonic similarity between cortical areas and (ii) that similar areas are more likely to be axonally connected, as predicted by the homophily principle. We first introduce the historical roots and technical foundations of MRI similarity analysis and compare it with the distinct MRI techniques of structural covariance and tractography analysis. We contextualize this empirical work with two generative models of homophilic networks: an economic model of cost-constrained connectional homophily and a heterochronic model of ontogenetically phased cortical maturation. We then review (i) studies of the genetic and transcriptional architecture of MRI similarity in population-averaged and disorder-specific contexts and (ii) developmental studies of normative cohorts and clinical studies of neurodevelopmental and neurodegenerative disorders. Finally, we prioritize knowledge gaps that must be addressed to consolidate structural MRI similarity as an accessible, valid marker of the architecture and connectivity of an individual brain network.

Abnormalities in cerebellar subregions' volume and cerebellocerebral structural covariance in autism spectrum disorder

The cerebellum plays a crucial role in functions, including sensory-motor coordination, cognition, and emotional processing. Compared to the neocortex, the human cerebellum exhibits a protracted developmental trajectory. This delayed developmental timeline may lead to increased sensitivity of the cerebellum to external influences, potentially extending the vulnerability period for neurological disorders. Abnormal cerebellar development in individuals with autism has been confirmed, and these atypical cerebellar changes may affect the development of the neocortex. However, due to the heterogeneity of autism spectrum disorder (ASD), the regional changes in the cerebellum and cerebellocerebral structural relationship remain unknown. To address these issues, we utilized imaging methods optimized for the cerebellum and cerebrum on 817 individuals aged 5-18 years in the ABIDE II dataset. After FDR correction, significant differences between groups were found in the right crus II/VIIB and vermis VI-VII. Structural covariance analysis revealed enhanced structural covariance in individuals with autism between the cerebellum and parahippocampal gyrus, pars opercularis, and transverse temporal gyrus in the right hemisphere after FDR correction. Furthermore, the structural covariance between the cerebellum and some regions of the cerebrum varied across sexes. A significant increase in structural covariance between the cerebellum and specific subcortical structures was also observed in individuals with ASD. Our study found atypical patterns in the structural covariance between the cerebellum and cerebrum in individuals with autism, which suggested that the underlying pathological processes of ASD might concurrently affect these brain regions. This study provided insight into the potential of cerebellocerebral pathways as therapeutic targets for ASD.

Patterns of Cerebellar-Cortical Structural Covariance Mirror Anatomical Connectivity of Sensorimotor and Cognitive Networks

The cortex and cerebellum are densely connected through reciprocal input/output projections that form segregated circuits. These circuits are shown to differentially connect anterior lobules of the cerebellum to sensorimotor regions, and lobules Crus I and II to prefrontal regions. This differential connectivity pattern leads to the hypothesis that individual differences in structure should be related, especially for connected regions. To test this hypothesis, we examined covariation between the volumes of anterior sensorimotor and lateral cognitive lobules of the cerebellum and measures of cortical thickness (CT) and surface area (SA) across the whole brain in a sample of 270 young adults drawn from the HCP dataset. We observed that patterns of cerebellar-cortical covariance differed between sensorimotor and cognitive networks. Anterior motor lobules of the cerebellum showed greater covariance with sensorimotor regions of the cortex, while lobules Crus I and Crus II showed greater covariance with frontal and temporal regions. Interestingly, cerebellar volume showed predominantly negative relationships with CT and predominantly positive relationships with SA. Individual differences in SA are thought to be largely under genetic control while CT is thought to be more malleable by experience. This suggests that cerebellar-cortical covariation for SA may be a more stable feature, whereas covariation for CT may be more affected by development. Additionally, similarity metrics revealed that the pattern of covariance showed a gradual transition between sensorimotor and cognitive lobules, consistent with evidence of functional gradients within the cerebellum. Taken together, these findings are consistent with known patterns of structural and functional connectivity between the cerebellum and cortex. They also shed new light on possibly differing relationships between cerebellar volume and cortical thickness and surface area. Finally, our findings are consistent with the interactive specialization framework which proposes that structurally and functionally connected brain regions develop in concert.

Impact of sleep disruptions on gray matter structural covariance networks across the Alzheimer's disease continuum

BACKGROUND

This study explores the impact of sleep disturbances on gray matter structural covariance networks (SCNs) across the Alzheimer's disease (AD) continuum.

METHODS

Amyloid-negative participants served as controls, whereas amyloid positive (A+) individuals were categorized into six groups based on cognitive status and sleep quality. SCNs for the default mode network (DMN), salience network (SN), and executive control network (ECN) were derived from T1-weighted magnetic resonance images.

RESULTS

In the DMN, increased structural associations were observed in cognitive unimpaired (CU) A+ and mild cognitive impairment (MCI) groups regardless of sleep quality, whereas AD with poor sleep (PS) showed a decrease and AD with normal sleep (NS) an increase. For the ECN, AD-NS showed increased and AD-PS showed reduced associations. In the SN, reduced associations were observed in CU A+ NS and MCI-NS, whereas AD-NS displayed increased associations; only AD-PS had decreased associations.

CONCLUSION

Distinct SCN damage patterns between normal and poor sleepers provide insights into sleep disturbances in AD.

Highlights

We delineated distinct patterns of structural covariance networks (SCN) impairment across the Alzheimer's disease (AD) continuum, uncovering significant disparities between individuals with normal sleep architecture and those afflicted by sleep disturbances.These observations underscore the pivotal importance of addressing sleep disruptions in AD therapeutics, providing a refined understanding of their detrimental impact on brain networks implicated in the disease.Our investigation epitomizes methodological precision by constructing an AD continuum using amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers to minimize diagnostic heterogeneity, further enhanced by a substantial cohort size that bolsters the robustness and generalizability of our findings.

Structural covariance network activity in the medial prefrontal cortex is modulated by childhood abuse in adolescents with depression

Aberrant structural covariance (SC) in the medial prefrontal cortex (mPFC) is believed to play a crucial role in adolescent-onset major depressive disorder (AO-MDD). However, the effect of childhood abuse (CA) on SC in AO-MDD patients is still unknown. Here, we measured anomalous SC in the mPFC of AO-MDD patients and assessed the potential modulation of this feature by CA. We acquired T1-weighted structural images of AO-MDD patients (n = 93) and healthy controls (HCs, n = 81). Using voxel-based morphometry analysis, we calculated gray matter volumes for each subject. Subsequently, we classified abnormal SC in the mPFC into three subtypes according to overall CA. Compared with HCs, AO-MDD patients showed alterations in the structural covariance network of the mPFC, which is a central region in the default mode network (DMN). We also found an anterior-posterior dissociation in the structural covariance connectivity of the DMN. A history of CA modulated bilateral mPFC SC. These changes were primarily focused on the SC between the mPFC and the limbic system, indicating a gap in the rate of neural maturation between these regions. In summary, the DMN and frontal-limbic system, which are involved in emotional processing, appear to play a significant role in the development of AO-MDD. These findings highlight the crucial effects of CA on neurophysiological alterations in individuals with AO-MDD.

Aberrant hippocampal intrinsic morphological connectivity patterns in Neuromyelitis optica spectrum disorder with cognitive impairment: Insights from an individual-based morphological brain network

BACKGROUND

Although several clinical studies have demonstrated that hippocampus volume loss in neuromyelitis optica spectrum disorder (NMOSD) may be a significant predictor of cognition, no consensus has been reached. To investigate the alterations of the intrinsic ,hippocampal morphological networks in cognitively impaired NMOSD patients and their correlations with cognitive performance.

METHODS

38 NMOSD patients and 39 healthy controls (HC) were enrolled. NMOSD patients were categorized into two groups based on neuropsychological assessment, including the cognitively impaired (CI) group (n = 21) and the cognitively preserved (CP) group (n = 17). Brain high-resolution 3D-T1WI MR images were evaluated, and individual-based intrinsic hippocampus morphological networks were constructed. The between-group differences in global and nodal network topology profiles were estimated, and correlations between the nodal network metrics and cognitive scores were further analyzed.

RESULTS

Compared to the HC and CP groups, the CI group shows significant differences in nodal network metrics of the left hippocampal tail and left hippocampal cornu ammonis (CA) 1-body. Nodal network metrics of the left hippocampal tail were significantly correlated with neurocognitive scores across the entire NMOSD group.

CONCLUSIONS

NMOSD patients with cognitive impairment exhibit abnormal intrinsic hippocampal morphological networks. Nodal network property measurements can help identify those with cognitive impairment.

Age-related changes of node degree in the multiple-demand network predict fluid intelligence

Fluid intelligence is an individual's innate ability to cope with complex situations and is gradually reduced across adults aging. The realization of fluid intelligence requires the simultaneous activity of multiple brain regions and depends on the structural connection of distributed brain regions. Uncovering the structural features of brain connections associated with fluid intelligence decline will provide reference for the development of intervention and treatment programs for cognitive decline. Using structural magnetic resonance imaging data of 454 healthy participants (18-87 years) from the Cam-CAN dataset, we constructed structural similarity network for each participant and calculated the node degree. Spearman correlation analysis showed that age was positively correlated with degree centrality in the cingulate cortex, left insula and subcortical regions, while negatively correlated with that in the orbito-frontal cortex, right middle temporal and precentral regions. Partial least squares (PLS) regression showed that the first PLS components explained 32 % (second PLS component: 20 %, p perm < 0.001) of the variance in fluid intelligence. Additionally, the degree centralities of anterior insula, supplementary motor area, prefrontal, orbito-frontal and anterior cingulate cortices, which are critical nodes of the multiple-demand network (MDN), were linked to fluid intelligence. Increased degree centrality in anterior cingulate cortex and left insula partially mediated age-related decline in fluid intelligence. Collectively, these findings suggest that the structural stability of MDN might contribute to the maintenance of fluid intelligence.

Decreased Functional and Structural Connectivity is Associated with Core Symptom Improvement in Children with Autism Spectrum Disorder After Mini-basketball Training Program

Exercise intervention has been proven helpful to ameliorate core symptoms of Autism Spectrum Disorder (ASD). However, the underlying mechanisms are not fully understood. In this study, we carried out a 12-week mini-basketball training program (MBTP) on ASD children and examined the changes of brain functional and structural networks before and after exercise intervention. We applied individual-based method to construct functional network and structural morphological network, and investigated their alterations following MBTP as well as their associations with the change in core symptom. Structural MRI and resting-state functional MRI data were obtained from 58 ASD children aged 3-12 years (experiment group: n = 32, control group: n = 26). ASD children who received MBTP intervention showed several distinguishable alternations compared to the control without special intervention. These included decreased functional connectivity within the sensorimotor network (SM) and between SM and the salience network, decreased morphological connectivity strength in a cortical-cortical network centered on the left inferior temporal gyrus, and a subcortical-cortical network centered on the left caudate. Particularly, the aforementioned functional and structural changes induced by MBTP were associated with core symptoms of ASD. Our findings suggested that MBTP intervention could be an effective approach to improve core symptoms in ASD children, decrease connectivity in both structure and function networks, and may drive the brain change towards normal-like neuroanatomy.

MQGA: A quantitative analysis of brain network hubs using multi-graph theoretical indices

Recent advancements in large-scale network studies have shown that connector hubs and provincial hubs are vital for coordinating complex cognitive tasks by facilitating information transfer between and within specialized modules. However, current methods for identifying these hubs often lack standardized measurement criteria, hindering quantitative analysis. This study proposes a novel computational method utilizing multi-graph theoretical index calculations to quantitatively analyze hub attributes in brain networks. Using benchmark network, random simulation network (N = 100), resting fMRI data from the ADHD-200 NYU dataset (HC = 110, ADHD = 146), and the Peking dataset (HC = 120, ADHD = 83), we introduce the Multi-criteria Quantitative Graph Analysis (MQGA) method, which employs betweenness centrality, degree centrality, and participation coefficient to determine the connector (con) hub index and provincial (pro) hub index. The method's accuracy, reliability, and stability were validated through correlation analysis of hub indices and labels, vulnerability tests, and consistency analysis across subjects. Results indicate that as network sparsity increases, the con hub index increases while the pro hub index decreases, with the optimal hub node index at 4 % sparsity. Vulnerability tests revealed that removing con nodes had a greater impact on network integrity than removing pro nodes. Both con and pro exhibited stability in consistency analyses, but con was more stable. The stability of hub scores in disease groups was significantly lower than in the healthy control group. High con values were found in the precuneus, postcentral gyrus, and precentral gyrus, whereas high pro values were identified in the precentral gyrus, postcentral gyrus, superior parietal lobule, precuneus, and superior temporal gyrus. This approach enhances the accuracy and sensitivity of hub node identification, facilitating precise comparisons and producing consistent, replicable results, advancing our understanding of brain network hub nodes, their roles in cognitive processes, and their implications for brain disease research.