Function-structure associations of the brain: evidence from multimodal connectivity and covariance studies

A spatially constrained independent component analysis jointly informed by structural and functional network connectivity

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

Multimodal covarying brain patterns mediate genetic and psychological contributions to individual differences in pain sensitivity

ABSTRACT

Individuals vary significantly in their pain sensitivity, with contributions from the brain, genes, and psychological factors. However, a multidimensional model integrating these factors is lacking due to their complex interactions. To address this, we measured pain sensitivity (ie, pain threshold and pain tolerance) using the cold pressor test, collected magnetic resonance imaging (MRI) data and genetic data, and evaluated psychological factors (ie, pain catastrophizing, pain-related fear, and pain-related anxiety) from 450 healthy participants with both sexes (160 male, 290 female). Using multimodal MRI fusion methods, we identified 2 pairs of covarying structural and functional brain patterns associated with pain threshold and tolerance, respectively. These patterns primarily involved regions related to self-awareness, sensory-discriminative, cognitive-evaluative, motion preparation and execution, and emotional aspects of pain. Notably, pain catastrophizing was negatively correlated with pain tolerance, and this relationship was mediated by the multimodal covarying brain patterns in male participants only. Furthermore, we identified an association between the single-nucleotide polymorphism rs4141964 within the fatty acid amide hydrolase gene and pain threshold, mediated by the identified multimodal covarying brain patterns across all participants. In summary, we suggested a model that integrates the brain, genes, and psychological factors to elucidate their role in shaping interindividual variations in pain sensitivity, highlighting the important contribution of the multimodal covarying brain patterns as important biological mediators in the associations between genes/psychological factors and pain sensitivity.

Changes of structural functional connectivity coupling and its correlations with cognitive function in patients with major depressive disorder

BACKGROUND

Previous neuroimaging studies have reported structural and functional brain abnormalities in major depressive disorder (MDD). This study aimed to explore whether the coherence of structural-functional networks was affected by disease and investigate its correlation with clinical manifestations.

METHODS

The severity of symptoms and cognitive function of 121 MDD patients and 139 healthy controls (HC) were assessed, and imaging data, including diffusion tensor imaging, T1 structural magnetic resonance imaging (MRI) and resting-state functional MRI, were collected. Spearman correlation coefficients of Kullback-Leibler similarity (KLS), fiber number (FN), fractional anisotropy (FA) and functional connectivity (FC) were calculated as coupling coefficients. Double-weight median correlation analysis was conducted to investigate the correlations between differences in brain networks and clinical assessments.

RESULTS

The percentage of total correct response of delayed matching to sample and the percentage of delayed correct response of pattern recognition memory was lower in MDD. Compared with the HC, KLS-FC coupling between the parietal lobe and subcortical area, FA-FC coupling between the temporal and parietal lobe, and FN-FC coupling in the frontal lobe was lower in MDD. Several correlations between structural-functional connectivity and clinical manifestations were identified.

LIMITATIONS

First, our study lacks longitudinal follow-up data. Second, the sample size was relatively small. Moreover, we only used the Anatomical Automatic Labeling template to construct the brain network. Finally, the validation of the causal relationship of neuroimaging-behavior factors was still insufficient.

CONCLUSIONS

The alternation in structural-functional coupling were related to clinical characterization and might be involved in the neuropathology of depression.

Analysis of Longitudinal Change Patterns in Developing Brain Using Functional and Structural Magnetic Resonance Imaging via Multimodal Fusion

Functional and structural magnetic resonance imaging (fMRI and sMRI) are complementary approaches that can be used to study longitudinal brain changes in adolescents. Each individual modality offers distinct insights into the brain. Each individual modality may overlook crucial aspects of brain analysis. By combining them, we can uncover hidden brain connections and gain a more comprehensive understanding. In previous work, we identified multivariate patterns of change in whole-brain function during adolescence. In this work, we focus on linking functional change patterns (FCPs) to brain structure. We introduce two approaches and applied them to data from the Adolescent Brain and Cognitive Development (ABCD) dataset. First, we evaluate voxelwise sMRI-FCP coupling to identify structural patterns linked to our previously identified FCPs. Our approach revealed multiple interesting patterns in functional network connectivity (FNC) and gray matter volume (GMV) data that were linked to subject level variation. FCP components 2 and 4 exhibit extensive associations between their loadings and voxel-wise GMV data. Secondly, we leveraged a symmetric multimodal fusion technique called multiset canonical correlation analysis (mCCA) + joint independent component analysis (jICA). Using this approach, we identify structured FCPs such as one showing increased connectivity between visual and sensorimotor domains and decreased connectivity between sensorimotor and cognitive control domains, linked to structural change patterns (SCPs) including alterations in the bilateral sensorimotor cortex. Interestingly, females exhibit stronger coupling between brain functional and structural changes than males, highlighting sex-related differences. The combined results from both asymmetric and symmetric multimodal fusion methods underscore the intricate sex-specific nuances in neural dynamics. By utilizing two complementary multimodal approaches, our study enhances our understanding of the dynamic nature of brain connectivity and structure during the adolescent period, shedding light on the nuanced processes underlying adolescent brain development.

Disrupted small-world white matter networks in patients with major depression and recent suicide plans or attempts

Suicide is a major concern for health, and depression is an established proximal risk factor for suicide. This study aimed to investigate white matter features associated with suicide. We constructed white matter structural networks by deterministic tractography via diffusion tensor imaging in 51 healthy controls, 47 depressed patients without suicide plans or attempts and 56 depressed patients with suicide plans or attempts. Then, graph theory analysis was used to measure global and nodal network properties. We found that local efficiency was decreased and path length was increased in suicidal depressed patients compared to healthy controls and non-suicidal depressed patients; moreover, the clustering coefficient was decreased in depressed patients compared to healthy controls; and the global efficiency and normalized characteristic path length was increased in suicidal depressed patients compared to healthy controls. Similarly, compared with those in non-suicidal depressed patients, nodal efficiency in the thalamus, caudate, medial orbitofrontal cortex, hippocampus, olfactory cortex, supplementary motor area and Rolandic operculum was decreased. In summary, compared with those of non-suicidal depressed patients, the structural connectome of suicidal depressed patients exhibited weakened integration and segregation and decreased nodal efficiency in the fronto-limbic-basal ganglia-thalamic circuitry. These alterations in the structural networks of depressed suicidal brains provide insights into the underlying neurobiology of brain features associated with suicide.

Cortical thickness moderates intraindividual variability in prefrontal cortex activation patterns of older adults during walking

OBJECTIVE

Increased intraindividual variability (IIV) in behavioral and cognitive performance is a risk factor for adverse outcomes but research concerning hemodynamic signal IIV is limited. Cortical thinning occurs during aging and is associated with cognitive decline. Dual-task walking (DTW) performance in older adults has been related to cognition and neural integrity. We examined the hypothesis that reduced cortical thickness would be associated with greater increases in IIV in prefrontal cortex oxygenated hemoglobin (HbO2) from single tasks to DTW in healthy older adults while adjusting for behavioral performance.

METHOD

Participants were 55 healthy community-dwelling older adults (mean age = 74.84, standard deviation (SD) = 4.97). Structural MRI was used to quantify cortical thickness. Functional near-infrared spectroscopy (fNIRS) was used to assess changes in prefrontal cortex HbO2 during walking. HbO2 IIV was operationalized as the SD of HbO2 observations assessed during the first 30 seconds of each task. Linear mixed models were used to examine the moderation effect of cortical thickness throughout the cortex on HbO2 IIV across task conditions.

RESULTS

Analyses revealed that thinner cortex in several regions was associated with greater increases in HbO2 IIV from the single tasks to DTW (ps < .02).

CONCLUSIONS

Consistent with neural inefficiency, reduced cortical thickness in the PFC and throughout the cerebral cortex was associated with increases in HbO2 IIV from the single tasks to DTW without behavioral benefit. Reduced cortical thickness and greater IIV of prefrontal cortex HbO2 during DTW may be further investigated as risk factors for developing mobility impairments in aging.

Voxel-wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification using Deep Multimodal Learning

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis for effective treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI have emerged as valuable tools for investigating AD-related brain alterations. However, the potential benefits of integrating these modalities using deep learning techniques remain unexplored. In this study, we propose a novel framework that fuses composite images of multiple rs-fMRI networks (called voxelwise intensity projection) and gray matter segmentation images through a deep learning approach for improved AD classification. We demonstrate the superiority of fMRI networks over commonly used metrics such as amplitude of low-frequency fluctuations (ALFF) and fractional ALFF in capturing spatial maps critical for AD classification. We use a multi-channel convolutional neural network incorporating the AlexNet dropout architecture to effectively model spatial and temporal dependencies in the integrated data. Extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset of AD patients and cognitively normal (CN) validate the efficacy of our approach, showcasing improved classification performance of 94.12% test accuracy and an area under the curve (AUC) score of 97.79 compared to existing methods. Our results show that the fusion results generally outperformed the unimodal results. The saliency visualizations also show significant differences in the hippocampus, amygdala, putamen, caudate nucleus, and regions of basal ganglia which are in line with the previous neurobiological literature. Our research offers a novel method to enhance our grasp of AD pathology. By integrating data from various functional networks with structural MRI insights, we significantly improve diagnostic accuracy. This accuracy is further boosted by the effective visualization of this combined information. This lays the groundwork for further studies focused on providing a more accurate and personalized approach to AD diagnosis. The proposed framework and insights gained from fMRI networks provide a promising avenue for future research in deep multimodal fusion and neuroimaging analysis.

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

The examination of multivariate brain morphometry patterns has gained attention in recent years, especially for their powerful exploratory capabilities in the study of differences between patients and controls. Among the many existing methods and tools for the analysis of brain anatomy based on structural magnetic resonance imaging data, data-driven source-based morphometry (SBM) focuses on the exploratory detection of such patterns. Here, we implement a semi-blind extension of SBM, called constrained source-based morphometry (constrained SBM), which enables the extraction of maximally independent reference-alike sources using the constrained independent component analysis (ICA) approach. To do this, we combine SBM with a set of reference components covering the full brain, derived from a large independent data set (UKBiobank), to provide a fully automated SBM framework. This also allows us to implement a federated version of constrained SBM (cSBM) to allow analysis of data that is not locally accessible. In our proposed decentralized constrained source-based morphometry (dcSBM), the original data never leaves the local site. Each site operates constrained ICA on its private local data using a common distributed computation platform. Next, an aggregator/master node aggregates the results estimated from each local site and applies statistical analysis to estimate the significance of the sources. Finally, we utilize two additional multisite patient data sets to validate our model by comparing the resulting group difference estimates from both cSBM and dcSBM.

Data-driven multimodal fusion: approaches and applications in psychiatric research

In the era of big data, where vast amounts of information are being generated and collected at an unprecedented rate, there is a pressing demand for innovative data-driven multi-modal fusion methods. These methods aim to integrate diverse neuroimaging perspectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric disorders. However, analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of data. This is where data-driven multi-modal fusion techniques come into play. By combining information from multiple modalities in a synergistic manner, these methods enable us to uncover hidden patterns and relationships that would otherwise remain unnoticed. In this paper, we present an extensive overview of data-driven multimodal fusion approaches with or without prior information, with specific emphasis on canonical correlation analysis and independent component analysis. The applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics, environment, cognition, and treatment outcomes across various brain disorders. After summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications, we further discuss the emerging neuroimaging analyzing trends in big data, such as N-way multimodal fusion, deep learning approaches, and clinical translation. Overall, multimodal fusion emerges as an imperative approach providing valuable insights into the underlying neural basis of mental disorders, which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

Brain structure and function in the fourth decade of life after extremely low birth weight: An MRI and EEG study

OBJECTIVE

To examine potential long-term effects of extremely low birth weight (ELBW; ≤ 1000 g) on adult brain structure, brain function, and cognitive-behavioral performance.

METHODS

A subset of survivors from the prospectively-followed McMaster ELBW Cohort (n = 23, MBW = 816 g) and their peers born at normal birth weight (NBW; ≥ 2500 g; n = 14, MBW = 3361 g) provided T1-weighted magnetic resonance imaging (MRI) brain scans, resting electroencephalographic (EEG) recordings, and behavioral responses to a face-processing task in their early thirties.

RESULTS

Visual discrimination accuracy for human faces, resting EEG alpha power, and long-distance alpha coherence were lower in ELBW survivors than NBW adults, and volumes of white matter hypointensities (WMH) were higher. Across groups, face-processing performance was correlated positively with posterior EEG spectral power and long-distance alpha and theta coherence, and negatively with WMH. The associations between face-processing scores and parietal alpha power and theta coherence were reduced after adjustment for WMH.

CONCLUSIONS

Electrocortical activity, brain functional connectivity, and higher-order processing ability may be negatively affected by WMH burden, which is greater in adults born extremely preterm.

SIGNIFICANCE

Decrements in electrocortical activity and behavioral performance in adult ELBW survivors may be partly explained by increased WMH volumes in this vulnerable population.

A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain

Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.

Hippocampal anterior- posterior shift in childhood and adolescence

Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.

A systematic review of the neural correlates of well-being reveals no consistent associations

Findings from behavioral and genetic studies indicate a potential role for the involvement of brain structures and brain functioning in well-being. We performed a systematic review on the association between brain structures or brain functioning and well-being, including 56 studies. The 11 electroencephalography (EEG) studies suggest a larger alpha asymmetry (more left than right brain activation) to be related to higher well-being. The 18 Magnetic Resonance Imaging (MRI) studies, 26 resting-state functional MRI studies and two functional near-infrared spectroscopy (fNIRS) studies identified a wide range of brain regions involved in well-being, but replication across studies was scarce, both in direction and strength of the associations. The inconsistency could result from small sample sizes of most studies and a possible wide-spread network of brain regions with small effects involved in well-being. Future directions include well-powered brain-wide association studies and innovative methods to more reliably measure brain activity in daily life.

Brain Structural Covariance Network in Asperger Syndrome Differs From Those in Autism Spectrum Disorder and Healthy Controls

Introduction

Autism is a heterogeneous neurodevelopmental disorder associated with social, cognitive and behavioral impairments. These impairments are often reported along with alteration of the brain structure such as abnormal changes in the grey matter (GM) density. However, it is not yet clear whether these changes could be used to differentiate various subtypes of autism spectrum disorder (ASD).

Method

We compared the regional changes of GM density in ASD, Asperger's Syndrome (AS) individuals and a group of healthy controls (HC). In addition to regional changes itself, the amount of GM density changes in one region as compared to other brain regions was also calculated. We hypothesized that this structural covariance network could differentiate the AS individuals from the ASD and HC groups. Therefore, statistical analysis was performed on the MRI data of 70 male subjects including 26 ASD (age=14-50, IQ=92-132), 16 AS (age=7-58, IQ=93-133) and 28 HC (age=9-39, IQ=95-144).

Result

The one-way ANOVA on the GM density of 116 anatomically separated regions showed significant differences among the groups. The pattern of structural covariance network indicated that covariation of GM density between the brain regions is altered in ASD.

Conclusion

This changed structural covariance could be considered as a reason for less efficient segregation and integration of information in the brain that could lead to cognitive dysfunctions in autism. We hope these findings could improve our understanding about the pathobiology of autism and may pave the way towards a more effective intervention paradigm.

Dynamic Brain Connectivity in Resting-State FMRI Using Spectral ICA and Graph Approach: Application to Healthy Controls and Multiple Sclerosis

Multiple sclerosis (MS) is a neuroinflammatory disease that involves structural and functional damage to the brain. It changes the functional connectivity of the brain between and within networks. Resting-state functional magnetic resonance imaging (fMRI) enables us to measure functional correlation and independence between different brain regions. In recent years, statistical methods, including independent component analysis (ICA) and graph-based analysis, have been widely used in fMRI studies. Furthermore, topological properties of the brain have been appeared as significant features of neuroscience studies. Most studies are focused on graph analysis and ICA methods, rather than considering spectral approaches. Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and healthy controls (HCs). For this purpose, a spectral ICA method is proposed to extract the nodes of the brain graph. Spectral ICA extracts more reliable components and decreases the processing time in calculation of the static brain connectivity. Compared to Infomax ICA, dynamic range and low-frequency to high-frequency power ratio (fALFF) show better results using the proposed ICA. It is also helpful in selection of the states for dynamic connectivity. Furthermore, the dynamic connectivity-based extracted components from spectral ICA are estimated using a mutual information method and based on correlation of sliding time-windowed on selected IC time courses. First-level and second-level connectivity states are calculated using correlations of connectivity strength between graph nodes (spectral ICA components). Finally, static and dynamic connectivity are analyzed based on correlation nodes percolated by an anatomical automatic labeling (AAL) atlas. Despite static and dynamic connectivity results of AAL correlations not showing any significant changes between MS and HC, our results based on spectral ICA in static and dynamic connectivity showed significantly decreased connectivity in MS patients in the anterior cingulate cortex, whereas it was significantly weaker in the core but stronger at the periphery of the posterior cingulate cortex.

Multimodal MRI reveals alterations of the anterior insula and posterior cingulate cortex in bipolar II disorders: A surface-based approach

BACKGROUND

Bipolar disorder (BD) is a mental disorder with severe implications for those affected and their families. Previous studies detected brain structural and functional alterations in BD patients. However, very few studies conducted a multimodal MRI fusion analysis, and little is known about the role of common anomalies in the connectivity of BD.

METHODS

We collected sMRI, rs-fMRI, and DTI data from 56 patients with unmedicated BD-II depression and 72 age-, sex- and handedness-matched healthy controls. We applied data-driven approaches to analyze multimodal MRI data and detected brain areas with significant group differences in cortical thickness (CT), amplitude of low frequency fluctuations (ALFF), and fractional anisotropy (FA) of the superficial white matter. We observed the common abnormal areas and took these areas as seeds to analyze the resting-state functional connectivity (RSFC) patterns in BD patients by overlapping these abnormal areas.

RESULTS

The BD patients showed two common abnormal areas: (1) the left anterior insula (AI) with abnormal CT and FA, and (2) the left posterior cingulate cortex (PCC) with abnormal CT and ALFF. Seed-based analyses showed RSFC between the left AI and left occipital sensory cortex, the left AI and left superior and inferior parietal cortex, and the left PCC and right medial prefrontal cortex were uniformly lower in the BD patients than controls. Correlation analyses showed negative correction between AI's FA and disease episodes and between AI's FA and disease duration in depressed BD-II patients.

CONCLUSIONS

We observed abnormal brain structural and functional properties in the left AI and left PCC in BD patients. The abnormal RSFC patterns may suggest sensory and cognitive dysfunction in BD.

Task- and resting-state fMRI studies in multiple sclerosis: From regions to systems and time-varying analysis. Current status and future perspective

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Functional MRI is able to detect adaptive and maladaptive abnormalities at different MS stages.

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Increased fMRI activity is a feature of early MS, while progressive exhaustion of adaptive mechanisms is detected later on in the disease.

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Collapse of long-range connections and impaired hub integration characterize MS network reorganization.

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Time-varying connectivity analysis provides useful and complementary pieces of information to static functional connectivity.

•

New perspectives might be the use of multimodal MRI and artificial intelligence.

Multiple sclerosis (MS) is a neurological disorder affecting the central nervous system and features extensive functional brain changes that are poorly understood but relate strongly to clinical impairments. Functional magnetic resonance imaging (fMRI) is a non-invasive, powerful technique able to map activity of brain regions and to assess how such regions interact for an efficient brain network. FMRI has been widely applied to study functional brain changes in MS, allowing to investigate functional plasticity consequent to disease-related structural injury. The first studies in MS using active fMRI tasks mainly aimed to study such plastic changes by identifying abnormal activity in salient brain regions (or systems) involved by the task. In later studies the focus shifted towards resting state (RS) functional connectivity (FC) studies, which aimed to map large-scale functional networks of the brain and to establish how MS pathology impairs functional integration, eventually leading to the hypothesized network collapse as patients clinically progress. This review provides a summary of the main findings from studies using task-based and RS fMRI and illustrates how functional brain alterations relate to clinical disability and cognitive deficits in this condition. We also give an overview of longitudinal studies that used task-based and RS fMRI to monitor disease evolution and effects of motor and cognitive rehabilitation. In addition, we discuss the results of studies using newer technologies involving time-varying FC to investigate abnormal dynamism and flexibility of network configurations in MS. Finally, we show some preliminary results from two recent topics (i.e., multimodal MRI analysis and artificial intelligence) that are receiving increasing attention. Together, these functional studies could provide new (conceptual) insights into disease stage-specific mechanisms underlying progression in MS, with recommendations for future research.

The Coupled Representation of Hierarchical Features for Mild Cognitive Impairment and Alzheimer's Disease Classification

Structural magnetic resonance imaging (MRI) features have played an increasingly crucial role in discriminating patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) from normal controls (NC). However, the large number of structural MRI studies only extracted low-level neuroimaging features or simply concatenated multitudinous features while ignoring the interregional covariate information. The appropriate representation and integration of multilevel features will be preferable for the precise discrimination in the progression of AD. In this study, we proposed a novel inter-coupled feature representation method and built an integration model considering the two-level (the regions of interest (ROI) level and the network level) coupled features based on structural MRI data. For the intra-coupled interactions about the network-level features, we performed the ROI-level (intra- and inter-) coupled interaction within each network by feature expansion and coupling learning. For the inter-coupled interaction of the network-level features, we measured the coupled relationships among different networks via Canonical correlation analysis. We evaluated the classification performance using coupled feature representations on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Results showed that the coupled integration model with hierarchical features achieved the optimal classification performance with an accuracy of 90.44% for AD and NC groups, with an accuracy of 87.72% for the MCI converter (MCI-c) and MCI non-converter (MCI-nc) groups. These findings suggested that our two-level coupled interaction representation of hierarchical features has been the effective means for the precise discrimination of MCI-c from MCI-nc groups and, therefore, helpful in the characterization of different AD courses.

The Classification Power of Classical and Intra-voxel Incoherent Motion (IVIM) Fitting Models of Diffusion-weighted Magnetic Resonance Images: An Experimental Study

This study aims at investigating the classification power of different b-values of the diffusion-weighted magnetic resonance images (DWI) as indicator of prostate cancer. This paper investigates several techniques for analyzing data from DWI acquired at a range of b-values for the purpose of detecting prostate cancer. In the first phase of experiments, we analyze the available data by producing two main parametric maps using two common models, namely: the intra-voxel incoherent motion (IVIM) model and the mono-exponential ADC model. Accordingly, we evaluated the benign/malignant tissue classification potential of several parametric maps produced using different combinations of b-values and fitting models. In the second phase, we utilized the maps that performed best in the first phase of experiments to design a machine learning-based computer-assisted diagnosis system for the detection of early stage prostate cancer. The system performance was cross-validated using data from 20 patients. On a fivefold cross-validation scheme, a maximum accuracy and an area under the receiver operating characteristic (AUC) of 90% and 0.978, respectively, was achieved by a system that uses ADC maps fitted using the mono-exponential model at 11 different b-values. The results suggest that the proposed machine learning-based diagnosis system is potentially powerful in differentiating between malignant and benign prostate tissues when combined with carefully generated ADC maps.

Combining advanced MRI and EEG techniques better explains long-term motor outcome after very preterm birth

BACKGROUND

Preterm born children are at high risk for adverse motor neurodevelopment. The aim of this study was to establish the relationship between motor outcome and advanced magnetic resonance imaging (MRI) and electroencephalography (EEG) measures.

METHODS

In a prospective cohort study of 64 very preterm born children, the motor outcome was assessed at 9.83 (SD 0.70) years. Volumetric MRI, diffusion tensor imaging (DTI), and EEG were acquired at 10.85 (SD 0.49) years. We investigated associations between motor outcome and brain volumes (white matter, deep gray matter, cerebellum, and ventricles), white matter integrity (fractional anisotropy and mean, axial and radial diffusivity), and brain activity (upper alpha (A2) functional connectivity and relative A2 power). The independence of associations with motor outcome was investigated with a final model. For each technique, the measure with the strongest association was selected to avoid multicollinearity.

RESULTS

Ventricular volume, radial diffusivity, mean diffusivity, relative A2 power, and A2 functional connectivity were significantly correlated to motor outcome. The final model showed that ventricular volume and relative A2 power were independently associated with motor outcome (B = -9.42 × 10^-5, p = 0.027 and B = 28.9, p = 0.007, respectively).

CONCLUSIONS

This study suggests that a lasting interplay exists between brain structure and function that might underlie motor outcome at school age.

IMPACT

This is the first study that investigates the relationships between motor outcome and brain volumes, DTI, and brain function in preterm born children at school age. Ventricular volume and relative upper alpha power on EEG have an independent relation with motor outcome in preterm born children at school age. This suggests that there is a lasting interplay between structure and function that underlies adverse motor outcome.

Longitudinal manganese-enhanced magnetic resonance imaging of neural projections and activity

Manganese-enhanced magnetic resonance imaging (MEMRI) holds exceptional promise for preclinical studies of brain-wide physiology in awake-behaving animals. The objectives of this review are to update the current information regarding MEMRI and to inform new investigators as to its potential. Mn(II) is a powerful contrast agent for two main reasons: (1) high signal intensity at low doses; and (2) biological interactions, such as projection tracing and neural activity mapping via entry into electrically active neurons in the living brain. High-spin Mn(II) reduces the relaxation time of water protons: at Mn(II) concentrations typically encountered in MEMRI, robust hyperintensity is obtained without adverse effects. By selectively entering neurons through voltage-gated calcium channels, Mn(II) highlights active neurons. Safe doses may be repeated over weeks to allow for longitudinal imaging of brain-wide dynamics in the same individual across time. When delivered by stereotactic intracerebral injection, Mn(II) enters active neurons at the injection site and then travels inside axons for long distances, tracing neuronal projection anatomy. Rates of axonal transport within the brain were measured for the first time in "time-lapse" MEMRI. When delivered systemically, Mn(II) enters active neurons throughout the brain via voltage-sensitive calcium channels and clears slowly. Thus behavior can be monitored during Mn(II) uptake and hyperintense signals due to Mn(II) uptake captured retrospectively, allowing pairing of behavior with neural activity maps for the first time. Here we review critical information gained from MEMRI projection mapping about human neuropsychological disorders. We then discuss results from neural activity mapping from systemic Mn(II) imaged longitudinally that have illuminated development of the tonotopic map in the inferior colliculus as well as brain-wide responses to acute threat and how it evolves over time. MEMRI posed specific challenges for image data analysis that have recently been transcended. We predict a bright future for longitudinal MEMRI in pursuit of solutions to the brain-behavior mystery.

Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults

The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.

In vivo Estimation of Axonal Morphology From Magnetic Resonance Imaging and Electroencephalography Data

Purpose

We present a novel approach that allows the estimation of morphological features of axonal fibers from data acquired in vivo in humans. This approach allows the assessment of white matter microscopic properties non-invasively with improved specificity.

Theory

The proposed approach is based on a biophysical model of Magnetic Resonance Imaging (MRI) data and of axonal conduction velocity estimates obtained with Electroencephalography (EEG). In a white matter tract of interest, these data depend on (1) the distribution of axonal radius [P(r)] and (2) the g-ratio of the individual axons that compose this tract [g(r)]. P(r) is assumed to follow a Gamma distribution with mode and scale parameters, M and θ, and g(r) is described by a power law with parameters α and β.

Methods

MRI and EEG data were recorded from 14 healthy volunteers. MRI data were collected with a 3T scanner. MRI-measured g-ratio maps were computed and sampled along the visual transcallosal tract. EEG data were recorded using a 128-lead system with a visual Poffenberg paradigm. The interhemispheric transfer time and axonal conduction velocity were computed from the EEG current density at the group level. Using the MRI and EEG measures and the proposed model, we estimated morphological properties of axons in the visual transcallosal tract.

Results

The estimated interhemispheric transfer time was 11.72 ± 2.87 ms, leading to an average conduction velocity across subjects of 13.22 ± 1.18 m/s. Out of the 4 free parameters of the proposed model, we estimated θ – the width of the right tail of the axonal radius distribution – and β – the scaling factor of the axonal g-ratio, a measure of fiber myelination. Across subjects, the parameter θ was 0.40 ± 0.07 μm and the parameter β was 0.67 ± 0.02 μm^−α.

Conclusion

The estimates of axonal radius and myelination are consistent with histological findings, illustrating the feasibility of this approach. The proposed method allows the measurement of the distribution of axonal radius and myelination within a white matter tract, opening new avenues for the combined study of brain structure and function, and for in vivo histological studies of the human brain.

A methodological scoping review of the integration of fMRI to guide dMRI tractography. What has been done and what can be improved: A 20-year perspective

Combining MRI modalities is a growing trend in neurosciences. It provides opportunities to investigate the brain architecture supporting cognitive functions. Integrating fMRI activation to guide dMRI tractography offers potential advantages over standard tractography methods. A quick glimpse of the literature on this topic reveals that this technique is challenging, and no consensus or "best practices" currently exist, at least not within a single document. We present the first attempt to systematically analyze and summarize the literature of 80 studies that integrated task-based fMRI results to guide tractography, over the last two decades. We report 19 findings that cover challenges related to sample size, microstructure modelling, seeding methods, multimodal space registration, false negatives/positives, specificity/validity, gray/white matter interface and more. These findings will help the scientific community (1) understand the strengths and limitations of the approaches, (2) design studies using this integrative framework, and (3) motivate researchers to fill the gaps identified. We provide references toward best practices, in order to improve the overall result's replicability, sensitivity, specificity, and validity.

Association between Changes in Cortical Thickness and Functional Connectivity in Male Patients with Alcohol-dependence

Many studies have reported structural or functional brain changes in patients with alcohol-dependence (ADPs). However, there has been an insufficient number of studies that were able to identify functional changes along with structural abnormalities in ADPs. Since neuronal cell death can lead to abnormal brain function, a multimodal approach combined with structural and functional studies is necessary to understand definitive neural mechanisms. Here, we explored regional difference in cortical thickness and their impact on functional connection along with clinical relevance. Fifteen male ADPs who have been diagnosed by the Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5) underwent high-resolution T1 and resting-state functional magnetic resonance imaging (MRI) scans together with 15 male healthy controls (HCs). The acquired MRI data were post-processed using the Computational Anatomy Toolbox (CAT 12) and CONN-fMRI functional connectivity (FC) toolbox with Statistical Parametric Mapping (SPM 12). When compared with male HCs, the male ADPs showed significantly reduced cortical thickness in the left postcentral gyrus (PoCG), an area responsible for altered resting-state FC patterns in male ADPs. Statistically higher FCs in PoCG-cerebellum (Cb) and lower FCs in PoCG-supplementary motor area (SMA) were observed in male ADPs. In particular, the FCs with PoCG-Cb positively correlated with alcohol use disorders identification test (AUDIT) scores in male ADPs. Our findings suggest that the association of brain structural abnormalities and FC changes could be a characteristic difference in male ADPs. These findings can be useful in understanding the neural mechanisms associated with anatomical, functional and clinical features of individuals with alcoholism.

Functional Topography of the Human Subthalamic Nucleus: Relevance for Subthalamotomy in Parkinson's Disease

BACKGROUND

The subthalamic nucleus (STN) is considered a key structure in motor, behavioral, and emotional control. Although identification of the functional topography of the STN has therapeutic implications in the treatment of the motor features of Parkinson's disease (PD), the details of its functional and somatotopic organization in humans are not well understood.

OBJECTIVE

The aim of this study was to characterize the functional organization of the STN and its correlation with the motor outcomes induced by subthalamotomy.

METHODS

We used diffusion-weighted imaging to assess STN connectivity patterns in 23 healthy control subjects and 86 patients with PD, of whom 39 received unilateral subthalamotomy. Analytical tractography was used to reconstruct structural cortico-subthalamic connectivity. A diffusion-weighted imaging/functional magnetic resonance imaging-driven somatotopic parcellation of the STN was defined to delineate the representation of the upper and lower limb in the STN.

RESULTS

We confirmed a connectional gradient to sensorimotor, supplementary-motor, associative, and limbic cortical regions, spanning from posterior-dorsal-lateral to anterior-ventral-medial portions of the STN, with intermediate overlapping zones. Functional magnetic resonance imaging-driven parcellation demonstrated dual segregation of motor cortico-subthalamic projections in humans. Moreover, the relationship between lesion topography and functional anatomy of the STN explains specific improvement in bradykinesia, rigidity, and tremor induced by subthalamotomy.

CONCLUSIONS

Our results support an interplay between segregation and integration of cortico-subthalamic projections, suggesting the coexistence of parallel and convergent information processing. Identifying the functional topography of the STN will facilitate better definition of the optimal location for functional neurosurgical approaches, that is, electrode placement and lesion location, and improve specific cardinal features in PD. © 2021 International Parkinson and Movement Disorder Society.

Reassessing associations between white matter and behaviour with multimodal microstructural imaging

Several studies have established specific relationships between White Matter (WM) and behaviour. However, these studies have typically focussed on fractional anisotropy (FA), a neuroimaging metric that is sensitive to multiple tissue properties, making it difficult to identify what biological aspects of WM may drive such relationships. Here, we carry out a pre-registered assessment of WM-behaviour relationships in 50 healthy individuals across multiple behavioural and anatomical domains, and complementing FA with myelin-sensitive quantitative MR modalities (MT, R1, R2∗).

Surprisingly, we only find support for predicted relationships between FA and behaviour in one of three pre-registered tests. For one behavioural domain, where we failed to detect an FA-behaviour correlation, we instead find evidence for a correlation between behaviour and R1. This hints that multimodal approaches are able to identify a wider range of WM-behaviour relationships than focusing on FA alone.

To test whether a common biological substrate such as myelin underlies WM-behaviour relationships, we then ran joint multimodal analyses, combining across all MRI parameters considered. No significant multimodal signatures were found and power analyses suggested that sample sizes of 40–200 may be required to detect such joint multimodal effects, depending on the task being considered.

These results demonstrate that FA-behaviour relationships from the literature can be replicated, but may not be easily generalisable across domains. Instead, multimodal microstructural imaging may be best placed to detect a wider range of WM-behaviour relationships, as different MRI modalities provide distinct biological sensitivities. Our findings highlight a broad heterogeneity in WM's relationship with behaviour, suggesting that variable biological effects may be shaping their interaction.

Fusion analysis of gray matter and white matter in subjective cognitive decline and mild cognitive impairment by multimodal CCA-joint ICA

BACKGROUND

Previous multimodal neuroimaging studies analyzed each dataset independently in subjective cognitive decline (SCD) and mild cognitive impairment (MCI), missing the cross-information. Multi-modal fusion analysis can provide more integral and comprehensive information regarding the brain. There has been a paucity of research on fusion analysis of sMRI and DTI in SCD and MCI.

MATERIALS AND METHODS

In the present study, we conducted fusion analysis of structural MRI and DTI by applying multimodal canonical correlation analysis with joint independent component analysis (mCCA-jICA) to capture the cross-information of gray matter (GM) and white matter (WM) in 62 SCD patients, 99 MCI patients, and 70 healthy controls (HCs). We further analyzed correlations between the mixing coefficients of mCCA-jICA and neuropsychological scores among the three groups.

RESULTS

A set of joint-discriminative independent components of GM and fractional anisotropy (FA) exhibited significant links between SCD and HCs, as well as between MCI and HCs. The covariant abnormalities primarily involved the frontal lobe/middle temporal gyrus/calcarine sulcus-anterior thalamic radiation/superior longitudinal fasciculus in SCD, and middle temporal gyrus/ fusiform gyrus/caudate necleus-forceps minor/anterior thalamic radiation in MCI. There was no significant difference between SCD and MCI groups.

CONCLUSIONS

The covariant GM-WM abnormalities in SCD and MCI were found in specific brain regions involved in cognitive processing, which confirms the simultaneous GM and WM changes underlying cognitive decline. These findings suggest that multimodal fusion analysis allows for a more comprehensive understanding of the association among different types of brain tissues and its crucial role in the neuropathological mechanism of SCD and MCI.

Calcarine as a bridge between brain function and structure in irritable bowel syndrome: A multiplex network analysis

BACKGROUND AND AIM

Jointly analyzing structural and functional brain networks enables a better understanding of pathological underpinnings of irritable bowel syndrome (IBS). Multiplex network analysis provides a novel framework to study complex networks consisting of different types of connectivity patterns in multimodal data.

METHODS

In the present work, we integrated functional and structural networks to a multiplex network. Then, the multiplex metrics and the inner-layer/inter-layer hub nodes were investigated through 34 patients with IBS and 33 healthy controls.

RESULTS

Significantly differential multiplex degree in both left and right parts of calcarine was found, and meanwhile, IBS patients lost inner-layer hub properties in these regions. In addition, the left fusiform was no longer practicing as an inner-layer hub node, while the right median cingulate acted as a new inner-layer hub node in the IBS patients. Besides, the right calcarine, which lost its inner-layer hub identity, became a new inter-layer hub node, and the multiplex degree of the left hippocampus, which lost its inter-layer hub identity in IBS patients, was significantly positively correlated with the IBS Symptom Severity Score scores.

CONCLUSIONS

Inner-layer hub nodes of multiplex networks were preferentially vulnerable, and some inner-layer hub nodes would convert into inter-layer hub nodes in IBS patients. Besides, the inter-layer hub nodes might be influenced by IBS severity and therefore converted to general nodes.

From mathematics to medicine: A practical primer on topological data analysis (TDA) and the development of related analytic tools for the functional discovery of latent structure in fMRI data

fMRI is the preeminent method for collecting signals from the human brain in vivo, for using these signals in the service of functional discovery, and relating these discoveries to anatomical structure. Numerous computational and mathematical techniques have been deployed to extract information from the fMRI signal. Yet, the application of Topological Data Analyses (TDA) remain limited to certain sub-areas such as connectomics (that is, with summarized versions of fMRI data). While connectomics is a natural and important area of application of TDA, applications of TDA in the service of extracting structure from the (non-summarized) fMRI data itself are heretofore nonexistent. “Structure” within fMRI data is determined by dynamic fluctuations in spatially distributed signals over time, and TDA is well positioned to help researchers better characterize mass dynamics of the signal by rigorously capturing shape within it. To accurately motivate this idea, we a) survey an established method in TDA (“persistent homology”) to reveal and describe how complex structures can be extracted from data sets generally, and b) describe how persistent homology can be applied specifically to fMRI data. We provide explanations for some of the mathematical underpinnings of TDA (with expository figures), building ideas in the following sequence: a) fMRI researchers can and should use TDA to extract structure from their data; b) this extraction serves an important role in the endeavor of functional discovery, and c) TDA approaches can complement other established approaches toward fMRI analyses (for which we provide examples). We also provide detailed applications of TDA to fMRI data collected using established paradigms, and offer our software pipeline for readers interested in emulating our methods. This working overview is both an inter-disciplinary synthesis of ideas (to draw researchers in TDA and fMRI toward each other) and a detailed description of methods that can motivate collaborative research.

Deep Fusion of Brain Structure-Function in Mild Cognitive Impairment

Multimodal fusion of different types of neural image data provides an irreplaceable opportunity to take advantages of complementary cross-modal information that may only partially be contained in single modality. To jointly analyze multimodal data, deep neural networks can be especially useful because many studies have suggested that deep learning strategy is very efficient to reveal complex and non-linear relations buried in the data. However, most deep models, e.g., convolutional neural network and its numerous extensions, can only operate on regular Euclidean data like voxels in 3D MRI. The interrelated and hidden structures that beyond the grid neighbors, such as brain connectivity, may be overlooked. Moreover, how to effectively incorporate neuroscience knowledge into multimodal data fusion with a single deep framework is understudied. In this work, we developed a graph-based deep neural network to simultaneously model brain structure and function in Mild Cognitive Impairment (MCI): the topology of the graph is initialized using structural network (from diffusion MRI) and iteratively updated by incorporating functional information (from functional MRI) to maximize the capability of differentiating MCI patients from elderly normal controls. This resulted in a new connectome by exploring "deep relations" between brain structure and function in MCI patients and we named it as Deep Brain Connectome. Though deep brain connectome is learned individually, it shows consistent patterns of alteration comparing to structural network at group level. With deep brain connectome, our developed deep model can achieve 92.7% classification accuracy on ADNI dataset.

Prefrontal cortex activation during dual-task walking in older adults is moderated by thickness of several cortical regions

Dual tasking, a defined facet of executive control processes, is subserved, in part, by the prefrontal cortex (PFC). Previous functional near-infrared spectroscopy (fNIRS) studies revealed elevated PFC oxygenated hemoglobin (HbO₂) under Dual-Task-Walk (DTW) compared to Single-Task Walk (STW) conditions. Based on the concept of neural inefficiency (i.e., greater activation coupled with similar or worse performance), we hypothesized that decreased cortical thickness across multiple brain regions would be associated with greater HbO₂ increases from STW to DTW. Participants were 55 healthy community-dwelling older adults, whose cortical thickness was measured via MRI. HbO₂ levels in the PFC, measured via fNIRS, were assessed during active walking under STW and DTW conditions. Statistical analyses were adjusted for demographics and behavioral performance. Linear mixed-effects models revealed that the increase in HbO₂ from STW to DTW was moderated by cortical thickness in several regions. Specifically, thinner cortex in specific regions of the frontal, parietal, temporal, and occipital lobes, cingulate cortex, and insula was associated with greater increases in HbO₂ levels from single to dual-task walking. In conclusion, participants with thinner cortex in regions implicated in higher order control of walking employed greater neural resources, as measured by increased HbO₂, in the PFC during DTW, without demonstrating benefits to behavioral performance. To our knowledge, this is the first study to examine cortical thickness as a marker of neural inefficiency during active walking.

Enhanced Sensitivity to Optimistic Cues is Manifested in Brain Structure: A Voxel-Based Morphometry Study

Recent research shows that congruent outcomes are more rapidly (and incongruent less rapidly) detected when individuals receive optimistic rather than pessimistic cues, an effect that was termed optimism robustness. In the current voxel-based morphometry study, we examined whether optimism robustness has a counterpart in brain structure. The participants' task was to detect two different letters (symbolizing monetary gain or loss) in a visual search matrix. Prior to each onset of the search matrix, two different verbal cues informed our participants about a high probability to gain (optimistic expectancy) or lose (pessimistic expectancy) money. The target presented was either congruent or incongruent with these induced expectancies. Optimism robustness revealed in the participants' reaction times correlated positively with gray matter volume (GMV) in brain regions involved in selective attention (medial visual association area, intraparietal sulcus), emphasizing the strong intertwinement of optimistic expectancies and attention deployment. In addition, GMV in the primary visual cortex diminished with increasing optimism robustness, in line with the interpretation of optimism robustness arising from a global, context-oriented perception. Future studies should address the malleability of these structural correlates of optimism robustness. Our results may assist in the identification of treatment targets in depression.

Integrating Multimodal and Longitudinal Neuroimaging Data with Multi-Source Network Representation Learning

Uncovering the complex network of the brain is of great interest to the field of neuroimaging. Mining from these rich datasets, scientists try to unveil the fundamental biological mechanisms in the human brain. However, neuroimaging data collected for constructing brain networks is generally costly, and thus extracting useful information from a limited sample size of brain networks is demanding. Currently, there are two common trends in neuroimaging data collection that could be exploited to gain more information: 1) multimodal data, and 2) longitudinal data. It has been shown that these two types of data provide complementary information. Nonetheless, it is challenging to learn brain network representations that can simultaneously capture network properties from multimodal as well as longitudinal datasets. Here we propose a general fusion framework for multi-source learning of brain networks - multimodal brain network fusion with longitudinal coupling (MMLC). In our framework, three layers of information are considered, including cross-sectional similarity, multimodal coupling, and longitudinal consistency. Specifically, we jointly factorize multimodal networks and construct a rotation-based constraint to couple network variance across time. We also adopt the consensus factorization as the group consistent pattern. Using two publicly available brain imaging datasets, we demonstrate that MMLC may better predict psychometric scores than some other state-of-the-art brain network representation learning algorithms. Additionally, the discovered significant brain regions are synergistic with previous literature. Our new approach may boost statistical power and sheds new light on neuroimaging network biomarkers for future psychometric prediction research by integrating longitudinal and multimodal neuroimaging data.

Brain Molecular Connectivity in Neurodegenerative Conditions

Positron emission tomography (PET) allows for the in vivo assessment of early brain functional and molecular changes in neurodegenerative conditions, representing a unique tool in the diagnostic workup. The increased use of multivariate PET imaging analysis approaches has provided the chance to investigate regional molecular processes and long-distance brain circuit functional interactions in the last decade. PET metabolic and neurotransmission connectome can reveal brain region interactions. This review is an overview of concepts and methods for PET molecular and metabolic covariance assessment with evidence in neurodegenerative conditions, including Alzheimer’s disease and Lewy bodies disease spectrum. We highlight the effects of environmental and biological factors on brain network organization. All of the above might contribute to innovative diagnostic tools and potential disease-modifying interventions.

Inhibition-directed multimodal imaging fusion patterns in adults with ADHD and its potential underlying "gene-brain-cognition" relationship

Aims

Inhibition deficits have been suggested to be a core cognitive impairment in attention‐deficit/hyperactivity disorder (ADHD). Exploring imaging patterns and the potential genetic components associated with inhibition deficits would definitely promote our understanding of the neuropathological mechanism of ADHD. This study aims to investigate the multimodal imaging fusion features related to inhibition deficits in adults with ADHD (aADHD) and to make an exploratory analysis of the role of inhibition‐related gene, NOS1, on those brain alterations.

Methods

Specifically, multisite canonical correlation analysis with reference plus joint independent component analysis (MCCAR + jICA) was conducted to identify the joint co‐varying gray matter volume (GMV) and the functional connectivity (FC) features related to inhibition in 69 aADHD and 44 healthy controls. Then, mediation analysis was employed to detect the relationship among inhibition‐related imaging features, NOS1 ex1f‐VNTR genotypes, and inhibition.

Results

Inhibition‐directed multimodal imaging fusion patterns of aADHD were reduced GMV and FC in inhibition network and increased GMV and FC in default mode network. The results showed a significant indirect effect of NOS1 ex1f‐VNTR on inhibition via FC component [effect size = −0.54 (SE = 0.29), 95% CI = −1.16 to −0.01]. In addition, the results indicated a significant indirect effect of GMV on the inhibition via FC component [effect size = 0.43 (SE = 0.23), 95% CI = 0.12 to 1.00].

Conclusion

The findings suggested that reduced GMV and FC in inhibition network and increased GMV and FC in default mode network were jointly responsible for inhibition deficits in aADHD. Both the NOS1 ex1f‐VNTR genotypes and GMV might influence the inhibition through the mediation effect of the aforementioned FC (NOS1/GMV^→FC→Inhibition).

Transdiagnostic neuroimaging markers of psychiatric risk: A narrative review

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We review the literature on neural correlates of a general psychopathology factor

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General psychopathology relates to structural and functional neurodevelopment

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Disrupted network connectivity maturation may underlie psychiatric vulnerability

Several decades of neuroimaging research in psychiatry have shed light on structural and functional neural abnormalities associated with individual psychiatric disorders. However, there is increasing evidence for substantial overlap in the patterns of neural dysfunction seen across disorders, suggesting that risk for psychiatric illness may be shared across diagnostic boundaries. Gaining insights on the existence of shared neural mechanisms which may transdiagnostically underlie psychopathology is important for psychiatric research in order to tease apart the unique and common aspects of different disorders, but also clinically, so as to help identify individuals early on who may be biologically vulnerable to psychiatric disorder in general. In this narrative review, we first evaluate recent studies investigating the functional and structural neural correlates of a general psychopathology factor, which is thought to reflect the shared variance across common mental health symptoms and therefore index psychiatric vulnerability. We then link insights from this research to existing meta-analytic evidence for shared patterns of neural dysfunction across categorical psychiatric disorders. We conclude by providing an integrative account of vulnerability to mental illness, whereby delayed or disrupted maturation of large-scale networks (particularly default-mode, executive, and sensorimotor networks), and more generally between-network connectivity, results in a compromised ability to integrate and switch between internally and externally focused tasks.

Changes in the anterior cingulate cortex in Crohn's disease: A neuroimaging perspective

INTRODUCTION

Evidence suggests that Crohn's disease (CD) pathophysiology goes beyond the gastrointestinal tract and is also strongly associated with the brain. In particular, the anterior cingulate cortex (ACC), which plays an integral role in the first brain as part of the default mode network (DMN) and pain matrix, shows abnormalities using multiple neuroimaging modalities. This review summarizes nine related studies that investigated changes in the ACC using structural magnetic resonance imaging, resting-state functional magnetic resonance imaging, and magnetic resonance spectroscopy.

METHODS

An extensive PubMed literature search was conducted from 1980 to August 2020. In a review of the articles identified, particular attention was paid to analysis methods, technical protocol characteristics, and specific changes in the ACC.

RESULTS

In terms of morphology, a decrease in gray matter volume and cortical thickness was observed along with an increase in local gyrification index. In terms of function, functional connectivity (FC) within the DMN was increased. FC between the ACC and the amygdala was decreased. Higher amplitudes of low-frequency fluctuation and graph theory results, including connectivity strength, clustering coefficient, and local efficiency, were detected. In terms of neurotransmitter changes, the concentrations of glutamate increased along with a decrease in gamma-aminobutyric acid, providing a rational explanation for abdominal pain. These changes may be attributed to stress, pain, and negative emotions, as well as changes in gut microbiota.

CONCLUSIONS

For patients with CD, the ACC demonstrates structural, functional, and metabolic changes. In terms of clinical findings, the ACC plays an important role in the onset of depression/anxiety and abdominal pain. Therefore, successful modulation of this pathway may guide treatment.

Arcuate fasciculus architecture is associated with individual differences in pre-attentive detection of unpredicted music changes

The mismatch negativity (MMN) is an event related brain potential (ERP) elicited by unpredicted sounds presented in a sequence of repeated auditory stimuli. The neural sources of the MMN have been previously attributed to a fronto-temporo-parietal network which crucially overlaps with the so-called auditory dorsal stream, involving inferior and middle frontal, inferior parietal, and superior and middle temporal regions. These cortical areas are structurally connected by the arcuate fasciculus (AF), a three-branch pathway supporting the feedback-feedforward loop involved in auditory-motor integration, auditory working memory, storage of acoustic templates, as well as comparison and update of those templates. Here, we characterized the individual differences in the white-matter macrostructural properties of the AF and explored their link to the electrophysiological marker of passive change detection gathered in a melodic multifeature MMN-EEG paradigm in 26 healthy young adults without musical training. Our results show that left fronto-temporal white-matter connectivity plays an important role in the pre-attentive detection of rhythm modulations within a melody. Previous studies have shown that this AF segment is also critical for language processing and learning. This strong coupling between structure and function in auditory change detection might be related to life-time linguistic (and possibly musical) exposure and experiences, as well as to timing processing specialization of the left auditory cortex. To the best of our knowledge, this is the first time in which the relationship between neurophysiological (EEG) and brain white-matter connectivity indexes using DTI-tractography are studied together. Thus, the present results, although still exploratory, add to the existing evidence on the importance of studying the constraints imposed on cognitive functions by the underlying structural connectivity.

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

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

Relationship between the disrupted topological efficiency of the structural brain connectome and glucose hypometabolism in normal aging

Normal aging is accompanied by structural degeneration and glucose hypometabolism in the human brain. However, the relationship between structural network disconnections and hypometabolism in normal aging remains largely unknown. In the present study, by combining MRI and PET techniques, we investigated the metabolic mechanism of the structural brain connectome and its relationship with normal aging in a cross-sectional, community-based cohort of 42 cognitively normal elderly individuals aged 57-84 years. The structural connectome was constructed based on diffusion MRI tractography, and the network efficiency metrics were quantified using graph theory analyses. FDG-PET scanning was performed to evaluate the glucose metabolic level in the cortical regions of the individuals. The results of this study demonstrated that both network efficiency and cortical metabolism decrease with age (both p < 0.05). In the subregions of the bilateral thalamus, significant correlations between nodal efficiency and cortical metabolism could be observed across subjects. Individual-level analyses indicated that brain regions with higher nodal efficiency tend to exhibit higher metabolic levels, implying a tight coupling between nodal efficiency and glucose metabolism (r = 0.56, p = 1.15 × 10^-21). Moreover, efficiency-metabolism coupling coefficient significantly increased with age (r = 0.44, p = 0.0046). Finally, the main findings were also reproducible in the ADNI dataset. Together, our results demonstrate a close coupling between structural brain connectivity and cortical metabolism in normal elderly individuals and provide new insight that improve the present understanding of the metabolic mechanisms of structural brain disconnections in normal aging.

Grant Report on the Effects of Childhood Maltreatment on Neurocircuitry in Adolescent Depression

This report describes the protocol for an ongoing project funded by the National Institutes of Health (R01MH108155) that is focused on effects of childhood maltreatment (MALTX) on neurocircuitry changes associated with adolescent major depressive disorder (MDD). Extant clinical and neuroimaging literature on MDD is reviewed, which has relied on heterogeneous samples that do not parse out the unique contribution of MALTX on neurobiological changes in MDD. Employing a 2 × 2 study design (controls with no MALTX or MDD, MALTX only, MDD only, and MDD + MALTX), and based on a cohesive theoretical model that incorporates behavioral, cognitive and neurobiological domains, we describe the multi-modal neuroimaging techniques used to test whether structural and functional alterations in the fronto-limbic and fronto-striatal circuits associated with adolescent MDD are moderated by MALTX. We hypothesize that MDD + MALTX youth will show alterations in the fronto-limbic circuit, with reduced connectivity between the amygdala (AMG) and the prefrontal cortex (PFC), as the AMG is sensitive to stress/threat during development. Participants with MDD will exhibit increased functional connectivity between the AMG and PFC due to self-referential negative emotions. Lastly, MDD + MALTX will only show changes in motivational/anticipatory aspects of the fronto-striatal circuit, and MDD will exhibit changes in motivational and consummatory/outcome aspects of reward-processing. Our goal is to identify distinct neural substrates associated with MDD due to MALTX compared to other causes, as these markers could be used to more effectively predict treatment outcome, index treatment response, and facilitate alternative treatments for adolescents who do not respond well to traditional approaches.

Altered spontaneous brain activity in essential tremor with and without resting tremor: a resting-state fMRI study

OBJECTIVES

Essential tremor with resting tremor (rET) often exhibits severer clinical features and more extensive functional impairment than essential tremor without resting tremor (ETwr). However, the pathophysiology of rET is still unclear. This study aims to use resting-state functional magnetic resonance imaging (rs-fMRI) to explore the alterations of brain activity between the drug-naïve patients of rET and ETwr.

METHODS

We recruited 19 patients with rET, 31 patients with ETwr and 25 healthy controls (HCs) to undergo a 3.0-T rs-fMRI examination. The differences of regional brain spontaneous activity between the rET, ETwr and HCs, as well as between total ET (rET + ETwr) and HCs were measured by amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF). The relationships between the altered brain measurements and the clinical scores were analyzed.

RESULTS

Compared with HCs, both ET subgroups showed significantly decreased ALFF or fALFF values in the basal ganglia, inferior orbitofrontal gyrus and insula. The rET group specifically showed decreased ALFF values in the hippocampus and motor cortices, while the ETwr group specifically evidenced increased ALFF and fALFF values in the cerebellum.

DISCUSSION

Regional spontaneous activity in rET and ETwr share common changes and have differences, which may suggest that the functional activities in the limbic system and cerebellum are different between the two subtypes. Improved insights into rET and ETwr subtypes and the different brain spontaneous activity will be valuable for improving our understanding of the pathophysiology of the disease.

Gender Differences in Connectome-based Predictions of Individualized Intelligence Quotient and Sub-domain Scores

Scores on intelligence tests are strongly predictive of various important life outcomes. However, the gender discrepancy on intelligence quotient (IQ) prediction using brain imaging variables has not been studied. To this aim, we predicted individual IQ scores for males and females separately using whole-brain functional connectivity (FC). Robust predictions of intellectual capabilities were achieved across three independent data sets (680 subjects) and two intelligence measurements (IQ and fluid intelligence) using the same model within each gender. Interestingly, we found that intelligence of males and females were underpinned by different neurobiological correlates, which are consistent with their respective superiority in cognitive domains (visuospatial vs verbal ability). In addition, the identified FC patterns are uniquely predictive on IQ and its sub-domain scores only within the same gender but neither for the opposite gender nor on the IQ-irrelevant measures such as temperament traits. Moreover, females exhibit significantly higher IQ predictability than males in the discovery cohort. This findings facilitate our understanding of the biological basis of intelligence by demonstrating that intelligence is underpinned by a variety of complex neural mechanisms that engage an interacting network of regions-particularly prefrontal-parietal and basal ganglia-whereas the network pattern differs between genders.

Fusion analysis of gray matter and white matter in bipolar disorder by multimodal CCA-joint ICA

BACKGROUND

Bipolar disorder (BD) patients show morphological abnormalities in gray matter (GM) and white matter (WM), which can be revealed by structure MRI (sMRI) and diffusion tensor imaging (DTI) respectively. However, previous studies on BD mainly relied on separated analysis of single neuroimaging modality, and it remains unclear how GM and WM covary to the abnormal brain structures of BD patients.

METHODS

We recorded multimodal sMRI-DTI data of 35 BD patients and 30 healthy controls (HC) and used multimodal canonical component analysis and joint independent component analysis (mCCA-jICA) to identify altered covariant structures in GM and WM of BD. Group-discriminative and joint group-discriminative independent components (ICs) were identified between BD and HC. Correlation analysis was performed between the mixing coefficients and behavioral index.

RESULTS

For BD patients, experiments results revealed that the GM atrophy in inferior frontal gyrus, right anterior cingulate gyrus and left superior frontal gyrus are associated with the WM integrity reduction in corticospinal tract and superior longitudinal fasciculus. Further, compared with HC, different correlation between mixing coefficients of ICs and age was observed for BD patients.

LIMITATIONS

The number of participants needs to be increased to more rigorously validate the results of this study, ideally from multiple sites. Functional imaging data could be utilized to explore structural-functional covariant pattern in BD. Possible confounding effect of medication.

CONCLUSIONS

We performed fusion analysis of sMRI and DTI and revealed covariant (GM-WM) structural patterns of BD patients. This study could be useful for developing more reliable neural biomarkers of BD.

White matter information flow mapping from diffusion MRI and EEG

The human brain can be described as a network of specialized and spatially distributed regions. The activity of individual regions can be estimated using electroencephalography and the structure of the network can be measured using diffusion magnetic resonance imaging. However, the communication between the different cortical regions occurring through the white matter, coined information flow, cannot be observed by either modalities independently. Here, we present a new method to infer information flow in the white matter of the brain from joint diffusion MRI and EEG measurements. This is made possible by the millisecond resolution of EEG which makes the transfer of information from one region to another observable. A subject specific Bayesian network is built which captures the possible interactions between brain regions at different times. This network encodes the connections between brain regions detected using diffusion MRI tractography derived white matter bundles and their associated delays. By injecting the EEG measurements as evidence into this model, we are able to estimate the directed dynamical functional connectivity whose delays are supported by the diffusion MRI derived structural connectivity. We present our results in the form of information flow diagrams that trace transient communication between cortical regions over a functional data window. The performance of our algorithm under different noise levels is assessed using receiver operating characteristic curves on simulated data. In addition, using the well-characterized visual motor network as grounds to test our model, we present the information flow obtained during a reaching task following left or right visual stimuli. These promising results present the transfer of information from the eyes to the primary motor cortex. The information flow obtained using our technique can also be projected back to the anatomy and animated to produce videos of the information path through the white matter, opening a new window into multi-modal dynamic brain connectivity.