Circular representation of human cortical networks for subject and population-level connectomic visualization

Real-time tractography: computation and visualization

Did you know that even though tractography is often considered a computationally expensive and offline process, the latest algorithms can now be performed in real-time without sacrificing accuracy? Interactive real-time tractography has proven to be valuable in surgical planning and has the potential to enhance neuromodulation therapies, highlighting the importance of speed and precision in the generation of tractograms. This demand has driven the development of nearly 50 visualization tools over the past two decades, with advances in interactive real-time tractography offering new possibilities and providing rich insights into brain connectivity.

Sexual and Metabolic Differences in Hippocampal Evolution: Alzheimer's Disease Implications

Sex differences in brain metabolism and their relationship to neurodegenerative diseases like Alzheimer's are an important emerging topic in neuroscience. Intrinsic anatomic and metabolic differences related to male and female physiology have been described, underscoring the importance of considering biological sex in studying brain metabolism and associated pathologies. The hippocampus is a key structure exhibiting sex differences in volume and connectivity. Adult neurogenesis in the dentate gyrus, dendritic spine density, and electrophysiological plasticity contribute to the hippocampus' remarkable plasticity. Glucose transporters GLUT3 and GLUT4 are expressed in human hippocampal neurons, with proper glucose metabolism being crucial for learning and memory. Sex hormones play a major role, with the aromatase enzyme that generates estradiol increasing in neurons and astrocytes as an endogenous neuroprotective mechanism. Inhibition of aromatase increases gliosis and neurodegeneration after brain injury. Genetic variants of aromatase may confer higher Alzheimer's risk. Estrogen replacement therapy in postmenopausal women prevents hippocampal hypometabolism and preserves memory. Insulin is also a key regulator of hippocampal glucose metabolism and cognitive processes. Dysregulation of the insulin-sensitive glucose transporter GLUT4 may explain the comorbidity between type II diabetes and Alzheimer's. GLUT4 colocalizes with the insulin-regulated aminopeptidase IRAP in neuronal vesicles, suggesting an activity-dependent glucose uptake mechanism. Sex differences in brain metabolism are an important factor in understanding neurodegenerative diseases, and future research must elucidate the underlying mechanisms and potential therapeutic implications of these differences.

Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning

Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinations are equivocal. Such individuals can benefit from novel accurate assessments that complement clinical diagnostics. We introduce a Bayesian machine learning classifier to identify concussion through cortico-cortical connectome mapping from magnetic resonance imaging in persons with quasi-normal cognition and without neuroradiological findings. Classifier features are generated from connectivity matrices specifying the mean fractional anisotropy of white matter connections linking brain structures. Each connection's saliency to classification was quantified by training individual classifier instantiations using a single feature type. The classifier was tested on a discovery sample of 92 healthy controls (HCs; 26 females, age μ ± σ: 39.8 ± 15.5 years) and 471 adult mTBI patients (158 females, age μ ± σ: 38.4 ± 5.9 years). Results were replicated in an independent validation sample of 256 HCs (149 females, age μ ± σ: 55.3 ± 12.1 years) and 126 patients with concussion (46 females, age μ ± σ: 39.0 ± 17.7 years). Classifier accuracy exceeds 99% in both samples, suggesting robust generalizability to new samples. Notably, 13 bilateral cortico-cortical connection pairs predict diagnostic status with accuracy exceeding 99% in both discovery and validation samples. Many such connection pairs are between prefrontal cortex structures, fronto-limbic and fronto-subcortical structures, and occipito-temporal structures in the ventral ("what") visual stream. This and related connectivity form a highly salient network of brain connections that is particularly vulnerable to concussion. Because these connections are important in mediating cognitive control, memory, and attention, our findings explain the high frequency of cognitive disturbances after concussion. Our classifier was trained and validated on concussed participants with cognitive profiles very similar to those of HCs. This suggests that the classifier can complement current diagnostics by providing independent information in clinical contexts where patients have quasi-normal cognition but where concussion diagnosis stands to benefit from additional evidence.

Differential tractography and whole brain connectometry in primary motor area gliomas resection: A feasibility study

OBJECTIVE

Establish the evolution of the connectome before and after resection of motor area glioma using a comparison of connectome maps and high-definition differential tractography (DifT).

METHODS

DifT was done using normalized quantitative anisotropy (NQA) with DSI Studio. The quantitative analysis involved obtaining mean NQA and fractional anisotropy (FA) values for the disrupted pathways tracing the corticospinal tract (CST), and white fiber network changes over time.

RESULTS

We described the baseline tractography, DifT, and white matter network changes from two patients who underwent resection of an oligodendroglioma (Case 1) and an IDH mutant astrocytoma, grade 4 (Case 2).

CASE 1

There was a slight decrease in the diffusion signal of the compromised CST in the immediate postop. The NQA and FA values increased at the 1-year follow-up (0.18 vs. 0.32 and 0.35 vs. 0.44, respectively).

CASE 2

There was an important decrease in the immediate postop, followed by an increase in the follow-up. In the 1-year follow-up, the patient presented with radiation necrosis and tumor recurrence, increasing NQA from 0.18 in the preop to 0.29. Fiber network analysis: whole-brain connectome comparison demonstrated no significant changes in the immediate postop. However, in the 1-year follow up there was a notorious reorganization of the fibers in both cases, showing the decreased density of connections.

CONCLUSIONS

Connectome studies and DifT constitute new potential tools to predict early reorganization changes in a patient's networks, showing the brain plasticity capacity, and helping to establish timelines for the progression of the tumor and treatment-induced changes.

Effects of Low-Level Light Therapy on Resting-State Connectivity Following Moderate Traumatic Brain Injury: Secondary Analyses of a Double-blinded Placebo-controlled Study

Background Low-level light therapy (LLLT) has been shown to modulate recovery in patients with traumatic brain injury (TBI). However, the impact of LLLT on the functional connectivity of the brain when at rest has not been well studied. Purpose To use functional MRI to assess the effect of LLLT on whole-brain resting-state functional connectivity (RSFC) in patients with moderate TBI at acute (within 1 week), subacute (2-3 weeks), and late-subacute (3 months) recovery phases. Materials and Methods This is a secondary analysis of a prospective single-site double-blinded sham-controlled study conducted in patients presenting to the emergency department with moderate TBI from November 2015 to July 2019. Participants were randomized for LLLT and sham treatment. The primary outcome of the study was to assess structural connectivity, and RSFC was collected as the secondary outcome. MRI was used to measure RSFC in 82 brain regions in participants during the three recovery phases. Healthy individuals who did not receive treatment were imaged at a single time point to provide control values. The Pearson correlation coefficient was estimated to assess the connectivity strength for each brain region pair, and estimates of the differences in Fisher z-transformed correlation coefficients (hereafter, z differences) were compared between recovery phases and treatment groups using a linear mixed-effects regression model. These analyses were repeated for all brain region pairs. False discovery rate (FDR)-adjusted P values were computed to account for multiple comparisons. Quantile mixed-effects models were constructed to quantify the association between the Rivermead Postconcussion Symptoms Questionnaire (RPQ) score, recovery phase, and treatment group. Results RSFC was evaluated in 17 LLLT-treated participants (median age, 50 years [IQR, 25-67 years]; nine female), 21 sham-treated participants (median age, 50 years [IQR, 43-59 years]; 11 female), and 23 healthy control participants (median age, 42 years [IQR, 32-54 years]; 13 male). Seven brain region pairs exhibited a greater change in connectivity in LLLT-treated participants than in sham-treated participants between the acute and subacute phases (range of z differences, 0.37 [95% CI: 0.20, 0.53] to 0.45 [95% CI: 0.24, 0.67]; FDR-adjusted P value range, .010-.047). Thirteen different brain region pairs showed an increase in connectivity in sham-treated participants between the subacute and late-subacute phases (range of z differences, 0.17 [95% CI: 0.09, 0.25] to 0.26 [95% CI: 0.14, 0.39]; FDR-adjusted P value range, .020-.047). There was no evidence of a difference in clinical outcomes between LLLT-treated and sham-treated participants (range of differences in medians, -3.54 [95% CI: -12.65, 5.57] to -0.59 [95% CI: -7.31, 8.49]; P value range, .44-.99), as measured according to RPQ scores. Conclusion Despite the small sample size, the change in RSFC from the acute to subacute phases of recovery was greater in LLLT-treated than sham-treated participants, suggesting that acute-phase LLLT may have an impact on resting-state neuronal circuits in the early recovery phase of moderate TBI. ClinicalTrials.gov Identifier: NCT02233413 © RSNA, 2024 Supplemental material is available for this article.

Voxel-based analysis: Roadmap for clinical translation

Voxel-based analysis (VBA) allows the full, 3-dimensional, dose distribution to be considered in radiotherapy outcome analysis. This provides new insights into anatomical variability of pathophysiology and radiosensitivity by removing the need for a priori definition of organs assumed to drive the dose response associated with patient outcomes. This approach may offer powerful biological insights demonstrating the heterogeneity of the radiobiology across tissues and potential associations of the radiotherapy dose with further factors. As this methodological approach becomes established, consideration needs to be given to translating VBA results to clinical implementation for patient benefit. Here, we present a comprehensive roadmap for VBA clinical translation. Technical validation needs to demonstrate robustness to methodology, where clinical validation must show generalisability to external datasets and link to a plausible pathophysiological hypothesis. Finally, clinical utility requires demonstration of potential benefit for patients in order for successful translation to be feasible. For each step on the roadmap, key considerations are discussed and recommendations provided for best practice.

Significant Acceleration of Regional Brain Aging and Atrophy After Mild Traumatic Brain Injury

Brain regions' rates of age-related volumetric change after traumatic brain injury (TBI) are unknown. Here, we quantify these rates cross-sectionally in 113 persons with recent mild TBI (mTBI), whom we compare against 3 418 healthy controls (HCs). Regional gray matter (GM) volumes were extracted from magnetic resonance images. Linear regression yielded regional brain ages and the annualized average rates of regional GM volume loss. These results were compared across groups after accounting for sex and intracranial volume. In HCs, the steepest rates of volume loss were recorded in the nucleus accumbens, amygdala, and lateral orbital sulcus. In mTBI, approximately 80% of GM structures had significantly steeper rates of annual volume loss than in HCs. The largest group differences involved the short gyri of the insula and both the long gyrus and central sulcus of the insula. No significant sex differences were found in the mTBI group, regional brain ages being the oldest in prefrontal and temporal structures. Thus, mTBI involves significantly steeper regional GM loss rates than in HCs, reflecting older-than-expected regional brain ages.

Improved psychosocial measures associated with physical activity may be explained by alterations in brain-gut microbiome signatures

Obesity contributes to physical comorbidities and mental health consequences. We explored whether physical activity could influence more than metabolic regulation and result in psychological benefits through the brain-gut microbiome (BGM) system in a population with high BMI. Fecal samples were obtained for 16 s rRNA profiling and fecal metabolomics, along with psychological and physical activity questionnaires. Whole brain resting-state functional MRI was acquired, and brain connectivity metrics were calculated. Higher physical activity was significantly associated with increased connectivity in inhibitory appetite control brain regions, while lower physical activity was associated with increased emotional regulation network connections. Higher physical activity was also associated with microbiome and metabolite signatures protective towards mental health and metabolic derangements. The greater resilience and coping, and lower levels of food addiction seen with higher physical activity, may be explained by BGM system differences. These novel findings provide an emphasis on the psychological and resilience benefits of physical activity, beyond metabolic regulation and these influences seem to be related to BGM interactions.

Regional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression

The biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region's contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯p2 for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (MAE ) and mean squared error (MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79. The regions whose volumes contribute most to BA are the nucleus accumbens (R¯p2=7.27%), inferior temporal gyrus (R¯p2=4.03%), thalamus (R¯p2=3.61%), brainstem (R¯p2=3.29%), posterior lateral sulcus (R¯p2=3.22%), caudate nucleus (R¯p2=3.05%), orbital gyrus (R¯p2=2.96%), and precentral gyrus (R¯p2=2.80%). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.

mRNA isoform balance in neuronal development and disease

Balanced mRNA isoform diversity and abundance are spatially and temporally regulated throughout cellular differentiation. The proportion of expressed isoforms contributes to cell type specification and determines key properties of the differentiated cells. Neurons are unique cell types with intricate developmental programs, characteristic cellular morphologies, and electrophysiological potential. Neuron-specific gene expression programs establish these distinctive cellular characteristics and drive diversity among neuronal subtypes. Genes with neuron-specific alternative processing are enriched in key neuronal functions, including synaptic proteins, adhesion molecules, and scaffold proteins. Despite the similarity of neuronal gene expression programs, each neuronal subclass can be distinguished by unique alternative mRNA processing events. Alternative processing of developmentally important transcripts alters coding and regulatory information, including interaction domains, transcript stability, subcellular localization, and targeting by RNA binding proteins. Fine-tuning of mRNA processing is essential for neuronal activity and maintenance. Thus, the focus of neuronal RNA biology research is to dissect the transcriptomic mechanisms that underlie neuronal homeostasis, and consequently, predispose neuronal subtypes to disease. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development.

Machine learning model to predict obesity using gut metabolite and brain microstructure data

A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions may contribute to obesity pathogenesis. In this study, we use a machine learning approach to leverage the enormous amount of microstructural neuroimaging and fecal metabolomic data to better understand key drivers of the obese compared to overweight phenotype. Our findings reveal that although gut-derived factors play a role in this distinction, it is primarily brain-directed changes that differentiate obese from overweight individuals. Of the key gut metabolites that emerged from our model, many are likely at least in part derived or influenced by the gut-microbiota, including some amino-acid derivatives. Remarkably, key regions outside of the central nervous system extended reward network emerged as important differentiators, suggesting a role for previously unexplored neural pathways in the pathogenesis of obesity.

Prenatal heroin exposure alters brain morphology and connectivity in adolescent mice

The United States is experiencing a dramatic increase in maternal opioid misuse and, consequently, the number of individuals exposed to opioids in utero. Prenatal opioid exposure has both acute and long-lasting effects on health and wellbeing. Effects on the brain, often identified at school age, manifest as cognitive impairment, attention deficit, and reduced scholastic achievement. The neurobiological basis for these effects is poorly understood. Here, we examine how in utero exposure to heroin affects brain development into early adolescence in a mouse model. Pregnant C57BL/6J mice received escalating doses of heroin twice daily on gestational days 4-18. The brains of offspring were assessed on postnatal day 28 using 9.4 T diffusion MRI of postmortem specimens at 36 μm resolution. Whole-brain volumes and the volumes of 166 bilateral regions were compared between heroin-exposed and control offspring. We identified a reduction in whole-brain volume in heroin-exposed offspring and heroin-associated volume changes in 29 regions after standardizing for whole-brain volume. Regions with bilaterally reduced standardized volumes in heroin-exposed offspring relative to controls include the ectorhinal and insular cortices. Regions with bilaterally increased standardized volumes in heroin-exposed offspring relative to controls include the periaqueductal gray, septal region, striatum, and hypothalamus. Leveraging microscopic resolution diffusion tensor imaging and precise regional parcellation, we generated whole-brain structural MRI diffusion connectomes. Using a dimension reduction approach with multivariate analysis of variance to assess group differences in the connectome, we found that in utero heroin exposure altered structure-based connectivity of the left septal region and the region that acts as a hub for limbic regulatory actions. Consistent with clinical evidence, our findings suggest that prenatal opioid exposure may have effects on brain morphology, connectivity, and, consequently, function that persist into adolescence. This work expands our understanding of the risks associated with opioid misuse during pregnancy and identifies biomarkers that may facilitate diagnosis and treatment.

Physical activity intensity is associated with cognition and functional connectivity in Parkinson's disease

BACKGROUND

Cognitive impairment is common in Parkinson's disease (PD) and often leads to dementia, with no effective treatment. Aging studies suggest that physical activity (PA) intensity has a positive impact on cognition and enhanced functional connectivity may underlie these benefits. However, less is known in PD. This cross-sectional study examined the relationship between PA intensity, cognitive performance, and resting state functional connectivity in PD and whether PA intensity influences the relationship between functional connectivity and cognitive performance.

METHODS

96 individuals with mild-moderate PD completed a comprehensive neuropsychological battery. Intensity of PA was objectively captured over a seven-day period using a wearable device (ActiGraph). Time spent in light and moderate intensity PA was determined based on standardized actigraphy cut points. Resting-state fMRI was assessed in a subset of 50 individuals to examine brain-wide functional connectivity.

RESULTS

Moderate intensity PA (MIPA), but not light PA, was associated with better global cognition, visuospatial function, memory, and executive function. Individuals who met the WHO recommendation of ≥150 min/week of MIPA demonstrated better global cognition, executive function, and visuospatial function. Resting-state functional connectivity associated with MIPA included a combination of brainstem, hippocampus, and regions in the frontal, cingulate, and parietal cortices, which showed higher connectivity across the brain in those achieving the WHO MIPA recommendation. Meeting this recommendation positively moderated the associations between identified functional connectivity and global cognition, visuospatial function, and language.

CONCLUSION

Encouraging MIPA, particularly the WHO recommendation of ≥150 min of MIPA/week, may represent an important prescription for PD cognition.

In vivo structural connectome of arousal and motor brainstem nuclei by 7 Tesla and 3 Tesla MRI

Brainstem nuclei are key participants in the generation and maintenance of arousal, which is a basic function that modulates wakefulness/sleep, autonomic responses, affect, attention, and consciousness. Their mechanism is based on diffuse pathways ascending from the brainstem to the thalamus, hypothalamus, basal forebrain and cortex. Several arousal brainstem nuclei also participate in motor functions that allow humans to respond and interact with the surrounding through a multipathway motor network. Yet, little is known about the structural connectivity of arousal and motor brainstem nuclei in living humans. This is due to the lack of appropriate tools able to accurately visualize brainstem nuclei in conventional imaging. Using a recently developed in vivo probabilistic brainstem nuclei atlas and 7 Tesla diffusion‐weighted images (DWI), we built the structural connectome of 18 arousal and motor brainstem nuclei in living humans (n = 19). Furthermore, to investigate the translatability of our findings to standard clinical MRI, we acquired 3 Tesla DWI on the same subjects, and measured the association of the connectome across scanners. For both arousal and motor circuits, our results showed high connectivity within brainstem nuclei, and with expected subcortical and cortical structures based on animal studies. The association between 3 Tesla and 7 Tesla connectivity values was good, especially within the brainstem. The resulting structural connectome might be used as a baseline to better understand arousal and motor functions in health and disease in humans.

Structural connectivity of autonomic, pain, limbic, and sensory brainstem nuclei in living humans based on 7 Tesla and 3 Tesla MRI

Autonomic, pain, limbic, and sensory processes are mainly governed by the central nervous system, with brainstem nuclei as relay centers for these crucial functions. Yet, the structural connectivity of brainstem nuclei in living humans remains understudied. These tiny structures are difficult to locate using conventional in vivo MRI, and ex vivo brainstem nuclei atlases lack precise and automatic transformability to in vivo images. To fill this gap, we mapped our recently developed probabilistic brainstem nuclei atlas developed in living humans to high‐spatial resolution (1.7 mm isotropic) and diffusion weighted imaging (DWI) at 7 Tesla in 20 healthy participants. To demonstrate clinical translatability, we also acquired 3 Tesla DWI with conventional resolution (2.5 mm isotropic) in the same participants. Results showed the structural connectome of 15 autonomic, pain, limbic, and sensory (including vestibular) brainstem nuclei/nuclei complex (superior/inferior colliculi, ventral tegmental area‐parabrachial pigmented, microcellular tegmental–parabigeminal, lateral/medial parabrachial, vestibular, superior olivary, superior/inferior medullary reticular formation, viscerosensory motor, raphe magnus/pallidus/obscurus, parvicellular reticular nucleus‐alpha part), derived from probabilistic tractography computation. Through graph measure analysis, we identified network hubs and demonstrated high intercommunity communication in these nuclei. We found good (r = .5) translational capability of the 7 Tesla connectome to clinical (i.e., 3 Tesla) datasets. Furthermore, we validated the structural connectome by building diagrams of autonomic/pain/limbic connectivity, vestibular connectivity, and their interactions, and by inspecting the presence of specific links based on human and animal literature. These findings offer a baseline for studies of these brainstem nuclei and their functions in health and disease, including autonomic dysfunction, chronic pain, psychiatric, and vestibular disorders.

Development and Testing of SPIDER-NET: An Interactive Tool for Brain Connectogram Visualization, Sub-Network Exploration and Graph Metrics Quantification

Brain connectomics consists in the modeling of human brain as networks, mathematically represented as numerical connectivity matrices. However, this representation may result in difficult interpretation of the data. To overcome this limitation, graphical representation by connectograms is currently used via open-source tools, which, however, lack user-friendly interfaces and options to explore specific sub-networks. In this context, we developed SPIDER-NET (Software Package Ideal for Deriving Enhanced Representations of brain NETworks), an easy-to-use, flexible, and interactive tool for connectograms generation and sub-network exploration. This study aims to present SPIDER-NET and to test its potential impact on pilot cases. As a working example, structural connectivity (SC) was investigated with SPIDER-NET in a group of 17 healthy controls (HCs) and in two subjects with stroke injury (Case 1 and Case 2, both with a focal lesion affecting part of the right frontal lobe, insular cortex and subcortical structures). 165 parcels were determined from individual structural magnetic resonance imaging data by using the Destrieux atlas, and defined as nodes. SC matrices were derived with Diffusion Tensor Imaging tractography. SC matrices of HCs were averaged to obtain a single group matrix. SC matrices were then used as input for SPIDER-NET. First, SPIDER-NET was used to derive the connectogram of the right hemisphere of Case 1 and Case 2. Then, a sub-network of interest (i.e., including gray matter regions affected by the stroke lesions) was interactively selected and the associated connectograms were derived for Case 1, Case 2 and HCs. Finally, graph-based metrics were derived for whole-brain SC matrices of Case 1, Case 2 and HCs. The software resulted effective in representing the expected (dis) connectivity pattern in the hemisphere affected by the stroke lesion in Cases 1 and 2. Furthermore, SPIDER-NET allowed to test an a priori hypothesis by interactively extracting a sub-network of interest: Case 1 showed a sub-network connectivity pattern different from Case 2, reflecting the different clinical severity. Global and local graph-based metrics derived with SPIDER-NET were different between cases with stroke injury and HCs. The tool proved to be accessible, intuitive, and interactive in brain connectivity investigation and provided both qualitative and quantitative evidence.

Functional connectome of brainstem nuclei involved in autonomic, limbic, pain and sensory processing in living humans from 7 Tesla resting state fMRI

Despite remarkable advances in mapping the functional connectivity of the cortex, the functional connectivity of subcortical regions is understudied in living humans. This is the case for brainstem nuclei that control vital processes, such as autonomic, limbic, nociceptive and sensory functions. This is because of the lack of precise brainstem nuclei localization, of adequate sensitivity and resolution in the deepest brain regions, as well as of optimized processing for the brainstem. To close the gap between the cortex and the brainstem, on 20 healthy subjects, we computed a correlation-based functional connectome of 15 brainstem nuclei involved in autonomic, limbic, nociceptive, and sensory function (superior and inferior colliculi, ventral tegmental area-parabrachial pigmented nucleus complex, microcellular tegmental nucleus-prabigeminal nucleus complex, lateral and medial parabrachial nuclei, vestibular and superior olivary complex, superior and inferior medullary reticular formation, viscerosensory motor nucleus, raphe magnus, pallidus, and obscurus, and parvicellular reticular nucleus - alpha part) with the rest of the brain. Specifically, we exploited 1.1mm isotropic resolution 7 Tesla resting-state fMRI, ad-hoc coregistration and physiological noise correction strategies, and a recently developed probabilistic template of brainstem nuclei. Further, we used 2.5mm isotropic resolution resting-state fMRI data acquired on a 3 Tesla scanner to assess the translatability of our results to conventional datasets. We report highly consistent correlation coefficients across subjects, confirming available literature on autonomic, limbic, nociceptive and sensory pathways, as well as high interconnectivity within the central autonomic network and the vestibular network. Interestingly, our results showed evidence of vestibulo-autonomic interactions in line with previous work. Comparison of 7 Tesla and 3 Tesla findings showed high translatability of results to conventional settings for brainstem-cortical connectivity and good yet weaker translatability for brainstem-brainstem connectivity. The brainstem functional connectome might bring new insight in the understanding of autonomic, limbic, nociceptive and sensory function in health and disease.

Aberrant rich club organization in patients with obsessive-compulsive disorder and their unaffected first-degree relatives

Recent studies suggested that the rich club organization promoting global brain communication and integration of information, may be abnormally increased in obsessive-compulsive disorder (OCD). However, the structural and functional basis of this organization is still not very clear. Given the heritability of OCD, as suggested by previous family-based studies, we hypothesize that aberrant rich club organization may be a trait marker for OCD. In the present study, 32 patients with OCD, 30 unaffected first-degree relatives (FDR) and 32 healthy controls (HC) underwent diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI). We examined the structural rich club organization and its interrelationship with functional coupling. Our results showed that rich club and peripheral connection strength in patients with OCD was lower than in HC, while it was intermediate in FDR. Finally, the coupling between structural and functional connections of the rich club, was decreased in FDR but not in OCD relative to HC, which suggests a buffering mechanism of brain functions in FDR. Overall, our findings suggest that alteration of the rich club organization may reflect a vulnerability biomarker for OCD, possibly buffered by structural and functional coupling of the rich club.

Track-Density Ratio Mapping With Fiber Types in the Cerebral Cortex Using Diffusion-Weighted MRI

The nerve fibers are divided into three categories: projection, commissural, and association fibers. This study demonstrated a novel cortical mapping method based on these three fiber categories using MR tractography data. The MR fiber-track data were extracted using the diffusion-weighted 3T-MRI data from 19 individuals’ Human Connectome Project dataset. Anatomical MR images in each dataset were parcellated using FreeSurfer software and Brainnetome atlas. The 5 million extracted tracks per subject by MRtrix software were classified based on the basic cortical structure (cortical area in the left and right hemisphere, subcortical area), after the tracks validation procedure. The number of terminals for each categorized track per unit-sized cortical area (1 mm³) was defined as the track-density in that cortical area. Track-density ratio mapping with fiber types was achieved by mapping the density-dependent color intensity for each categorized tracks with a different primary color. The mapping results showed a highly localized, unique density ratio map determined by fiber types. Furthermore, the quantitative group data analysis based on the parcellation information revealed that the majority of nerve fibers in the brain are association fibers, particularly in temporal, inferior parietal, and occipital lobes, while the projection and commissural fibers were mainly located in the superior part of the brain. Hemispheric asymmetries in the fiber density were also observed, such as long association fiber in the Broca’s and Wernicke’s areas. We believe this new dimensional brain mapping information allows us to further understand brain anatomy, function.

Radiation Pneumonitis in Thoracic Cancer Patients: Multi-Center Voxel-Based Analysis

Simple Summary

The pathophysiology of radiation pneumonitis (RP) after thoracic cancer radiation treatments is still not completely understood although the identification of underlying RP mechanisms may improve the therapeutic window of thoracic cancer patients. The aim of our retrospective study was to explore the dose–response patterns associated with RP by a multi-center voxel-based analysis. In a heterogeneously treated population of 382 thoracic cancer patients, we confirmed the previously described heart–lung interaction in the development of RP. The empowerment of VBA with a novel description of dose map spatial properties based on probabilistic independent component analysis (PICA) and connectograms provided valuable additional and independent information on the radiobiology of RP.

Abstract

This study investigates the dose–response patterns associated with radiation pneumonitis (RP) in patients treated for thoracic malignancies with different radiation modalities. To this end, voxel-based analysis (VBA) empowered by a novel strategy for the characterization of spatial properties of dose maps was applied. Data from 382 lung cancer and mediastinal lymphoma patients from three institutions treated with different radiation therapy (RT) techniques were analyzed. Each planning CT and biologically effective dose map (α/β = 3 Gy) was spatially normalized on a common anatomical reference. The VBA of local dose differences between patients with and without RP was performed and the clusters of voxels with dose differences that significantly correlated with RP at a p-level of 0.05 were generated accordingly. The robustness of VBA inference was evaluated by a novel characterization for spatial properties of dose maps based on probabilistic independent component analysis (PICA) and connectograms. This lays robust foundations to the obtained findings that the lower parts of the lungs and the heart play a prominent role in the development of RP. Connectograms showed that the dataset can support a radiobiological differentiation between the main heart and lung substructures.

Probing thoracic dose patterns associated to pericardial effusion and mortality in patients treated with photons and protons for locally advanced non-small-cell lung cancer

PURPOSE

To investigate thoracic dose-response patterns for pericardial effusion (PCE) and mortality in patients treated for locally advanced Non-Small-Cell Lung Cancer (NSCLC) by Intensity Modulated RT (IMRT) or Passive-Scattering Proton Therapy (PSPT).

METHODS

Among 178 patients, 43.5% developed grade ≥ 2 PCE. Clinical and dosimetric factors associated with PCE or overall survival (OS) were identified via multi-variable Cox proportional hazards modeling. The Voxel-Based Analyses (VBAs) of local dose differences between patients with and without PCE and mortality was performed. The robustness of VBA results was assessed by a novel characterization of spatial properties of dose distributions based on probabilistic independent component analysis (PICA) and connectograms.

RESULTS

Several non-dosimetric variables were selected by the multivariable analysis for the considered outcomes, while the time-dependent PCE onset was uncorrelated with the OS (p = 0.34) at a multi-variable Cox analysis. Despite the significant PSPT dosimetric advantage, the RT technique did not affect the occurrence of PCE or OS. VBAs highlighted largely overlapping clusters significantly associated with PCE endpoints in heart and lungs. No significant dosimetric patterns related to mortality endpoints were found. PICA identified 43 components homogeneously scattered within thorax, while connectograms showed modest correlations between doses in main cardio-pulmonary substructures.

CONCLUSIONS

Spatially resolved analysis highlighted dose patterns related to radiation-induced cardiac toxiciy and the observed organ-based dose-response mismatch in PSPT and IMRT. Indeed, the thoracic regions spared by PSPT poorly overlapped with the areas involved in PCE development, as highlited by VBA. PICA and connectograms proved valuable tools for assessing the robusteness of obtained VBA inferences.

Altered brain structural connectivity in patients with longstanding gut inflammation is correlated with psychological symptoms and disease duration

OBJECTIVE

We aimed to identify differences in network properties of white matter microstructure between asymptomatic ulcerative colitis (UC) participants who had a history of chronic gut inflammation, healthy controls (HCs) and a disease control group without gut inflammation (irritable bowel syndrome; IBS).

DESIGN

Diffusion weighted imaging was conducted in age and sex-matched participants with UC, IBS, and HCs (N = 74 each), together with measures of gastrointestinal and psychological symptom severity. Using streamline connectivity matrices and graph theory, we aimed to quantify group differences in brain network connectivity. Regions showing group connectivity differences were correlated with measures showing group behavioral and clinical differences.

RESULTS

UC participants exhibited greater centrality in regions of the somatosensory network and default mode network, but lower centrality in the posterior insula and globus pallidus compared to HCs (q < 0.05). Hub analyses revealed compromised hubness of the pallidus in UC and IBS compared to HCs which was replaced by increased hubness of the postcentral sulcus. Surprisingly, few differences in network matrices between UC and IBS were identified. In UC, centrality measures in the secondary somatosensory cortex were associated with depression (q < 0.03), symptom related anxiety (q < 0.04), trait anxiety (q < 0.03), and symptom duration (q < 0.05).

CONCLUSION

A history of UC is associated with neuroplastic changes in several brain networks, which are associated with symptoms of depression, trait and symptom-related anxiety, as well as symptom duration. When viewed together with the results from IBS subjects, these findings suggest that chronic gut inflammation as well as abdominal pain have a lasting impact on brain network organization, which may play a role in symptoms reported by UC patients, even when gut inflammation has subsided.

Peripheral ERK modulates acupuncture-induced brain neural activity and its functional connectivity

Acupuncture has been widely used as a therapeutic intervention, and the brain network plays a crucial role in its neural mechanism. This study aimed to investigate the acupuncture mechanism from peripheral to central by identifying how the peripheral molecular signals induced by acupuncture affect the brain neural responses and its functional connectivity. We confirmed that peripheral ERK activation by acupuncture plays a role in initiating acupuncture-induced peripheral proteomic changes in mice. The brain neural activities in the neocortex, hippocampus, thalamus, hypothalamus, periaqueductal grey, and nucleus of the solitary tract (Sol) were significantly changed after acupuncture, and these were altered by peripheral MEK/MAPK inhibition. The arcuate nucleus and lateral hypothalamus were the most affected by acupuncture and peripheral MEK/MAPK inhibition. The hypothalamic area was the most contributing brain region in contrast task PLS analysis. Acupuncture provoked extensive changes in brain functional connectivity, and the posterior hypothalamus showed the highest betweenness centrality after acupuncture. After brain hub identification, the Sol and cingulate cortex were selected as hub regions that reflect both degree and betweenness centrality after acupuncture. These results suggest that acupuncture activates brain functional connectivity and that peripheral ERK induced by acupuncture plays a role in initiating brain neural activation and its functional connectivity.

Alterations in reward network functional connectivity are associated with increased food addiction in obese individuals

Functional neuroimaging studies in obesity have identified alterations in the connectivity within the reward network leading to decreased homeostatic control of ingestive behavior. However, the neural mechanisms underlying sex differences in the prevalence of food addiction in obesity is unknown. The aim of the study was to identify functional connectivity alterations associated with: (1) Food addiction, (2) Sex- differences in food addiction, (3) Ingestive behaviors. 150 participants (females: N = 103, males: N = 47; food addiction: N = 40, no food addiction: N = 110) with high BMI ≥ 25 kg/m2 underwent functional resting state MRIs. Participants were administered the Yale Food Addiction Scale (YFAS), to determine diagnostic criteria for food addiction (YFAS Symptom Count ≥ 3 with clinically significant impairment or distress), and completed ingestive behavior questionnaires. Connectivity differences were analyzed using a general linear model in the CONN Toolbox and images were segmented using the Schaefer 400, Harvard-Oxford Subcortical, and Ascending Arousal Network atlases. Significant connectivities and clinical variables were correlated. Statistical significance was corrected for multiple comparisons at q < .05. (1) Individuals with food addiction had greater connectivity between brainstem regions and the orbital frontal gyrus compared to individuals with no food addiction. (2) Females with food addiction had greater connectivity in the salience and emotional regulation networks and lowered connectivity between the default mode network and central executive network compared to males with food addiction. (3) Increased connectivity between regions of the reward network was positively associated with scores on the General Food Cravings Questionnaire-Trait, indicative of greater food cravings in individuals with food addiction. Individuals with food addiction showed greater connectivity between regions of the reward network suggesting dysregulation of the dopaminergic pathway. Additionally, greater connectivity in the locus coeruleus could indicate that the maladaptive food behaviors displayed by individuals with food addiction serve as a coping mechanism in response to pathological anxiety and stress. Sex differences in functional connectivity suggest that females with food addiction engage more in emotional overeating and less cognitive control and homeostatic processing compared to males. These mechanistic pathways may have clinical implications for understanding the sex-dependent variability in response to diet interventions.

Mapping the rest of the human connectome: Atlasing the spinal cord and peripheral nervous system

The emergence of diffusion, structural, and functional neuroimaging methods has enabled major multi-site efforts to map the human connectome, which has heretofore been defined as containing all neural connections in the central nervous system (CNS). However, these efforts are not structured to examine the richness and complexity of the peripheral nervous system (PNS), which arguably forms the (neglected) rest of the connectome. Despite increasing interest in an atlas of the spinal cord (SC) and PNS which is simultaneously stereotactic, interactive, electronically dissectible, scalable, population-based and deformable, little attention has thus far been devoted to this task of critical importance. Nevertheless, the atlasing of these complete neural structures is essential for neurosurgical planning, neurological localization, and for mapping those components of the human connectome located outside of the CNS. Here we recommend a modification to the definition of the human connectome to include the SC and PNS, and argue for the creation of an inclusive atlas to complement current efforts to map the brain's human connectome, to enhance clinical education, and to assist progress in neuroscience research. In addition to providing a critical overview of existing neuroimaging techniques, image processing methodologies and algorithmic advances which can be combined for the creation of a full connectome atlas, we outline a blueprint for ultimately mapping the entire human nervous system and, thereby, for filling a critical gap in our scientific knowledge of neural connectivity.

Resolving the Connectome, Spectrally-Specific Functional Connectivity Networks and Their Distinct Contributions to Behavior

The resting human brain exhibits spontaneous patterns of activity that reflect features of the underlying neural substrate. Examination of interareal coupling of resting-state oscillatory activity has revealed that the brain’s resting activity is composed of functional networks, whose topographies differ depending on oscillatory frequency, suggesting a role for carrier frequency as a means of creating multiplexed, or functionally segregated, communication channels between brain areas. Using canonical correlation analysis (CCA), we examined spectrally resolved resting-state connectivity patterns derived from magnetoencephalography (MEG) recordings to determine the relationship between connectivity intrinsic to different frequency channels and a battery of over a hundred behavioral and demographic indicators, in a group of 89 young healthy participants. We demonstrate that each of the classical frequency bands in the range 1–40 Hz (δ, θ, α, β, and γ) delineates a subnetwork that is behaviorally relevant, spatially distinct, and whose expression is either negatively or positively predictive of individual traits, with the strongest link in the α-band being negative and networks oscillating at different frequencies, such as θ, β, and γ carrying positive function.

Altered structural connectivity networks in a mouse model of complete and partial dysgenesis of the corpus callosum

Corpus callosum dysgenesis (CCD) describes a collection of brain malformations in which the main fiber tract connecting the two hemispheres is either absent (complete CCD, or 'agenesis of the corpus callosum') or reduced in size (partial CCD). Humans with these neurodevelopmental disorders have a wide range of cognitive outcomes, including seemingly preserved features of interhemispheric communication in some cases. However, the structural substrates that could underlie this variability in outcome remain to be fully elucidated. Here, for the first time, we characterize the global brain connectivity of a mouse model of complete and partial CCD. We demonstrate features of structural brain connectivity that model those predicted in humans with CCD, including Probst bundles in complete CCD and heterotopic sigmoidal connections in partial CCD. Crucially, we also histologically validate the recently predicted ectopic sigmoid bundle present in humans with partial CCD, validating the utility of this mouse model for fine anatomical studies of this disorder. Taken together, this work describes a mouse model of altered structural connectivity in variable severity CCD and forms a foundation for future studies investigating the function and mechanisms of development of plastic tracts in developmental disorders of brain connectivity.

A Distinct Brain-Gut-Microbiome Profile Exists for Females with Obesity and Food Addiction

BACKGROUND

Alterations in brain-gut-microbiome interactions have been implicated as an important factor in obesity. This study aimed to explore the relationship between food addiction (FA) and the brain-gut-microbiome axis, using a multi-omics approach involving microbiome data, metabolomics, and brain imaging.

METHODS

Brain magnetic resonance imaging was obtained in 105 females. FA was defined by using the Yale Food Addiction Scale. Fecal samples were collected for sequencing and metabolomics. Statistical analysis was done by using multivariate analyses and machine learning algorithms.

RESULTS

Of the females with obesity, 33.3% exhibited FA as compared with 5.3% and 0.0% of females with overweight and normal BMI, respectively (P = 0.0001). Based on a multilevel sparse partial least square discriminant analysis, there was a difference in the gut microbiome of females with FA versus those without. Differential abundance testing showed Bacteroides, Megamonas, Eubacterium, and Akkermansia were statistically associated with FA (q < 0.05). Metabolomics showed that indolepropionic acid was inversely correlated with FA. FA was also correlated with increased connectivity within the brain's reward network, specifically between the intraparietal sulcus, brain stem, and putamen.

CONCLUSIONS

This is the first study to examine FA along the brain-gut-microbiome axis and it supports the idea of targeting the brain-gut-microbiome axis for the treatment of FA and obesity.

Distinct patterns of thought mediate the link between brain functional connectomes and well-being

Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort (n = 211), we identified two distinct patterns of thought, broadly describing the participants' current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.

Individual Resting-State Brain Networks Enabled by Massive Multivariate Conditional Mutual Information

Individual-level resting-state networks (RSNs) based on resting-state fMRI (rs-fMRI) are of great interest due to evidence that network dysfunction may underlie some diseases. Most current rs-fMRI analyses use linear correlation. Since correlation is a bivariate measure of association, it discards most of the information contained in the spatial variation of the thousands of hemodynamic signals within the voxels in a given brain region. Subject-specific functional RSNs using typical rs-fMRI data, are therefore dominated by indirect connections and loss of spatial information and can only deliver reliable connectivity after group averaging. While bivariate partial correlation can rule out indirect connections, it results in connectivity that is too sparse due to lack of sensitivity. We have developed a method that uses all the spatial variation information in a given parcel by employing a multivariate information-theoretic association measure based on canonical correlations. Our method, multivariate conditional mutual information (mvCMI) reliably constructs single-subject connectivity estimates showing mostly direct connections. Averaging across subjects is not needed. The method is applied to Human Connectome Project data and compared to diffusion MRI. The results are far superior to those obtained by correlation and partial correlation.

The response relevance of visual stimuli modulates the P3 component and the underlying sensorimotor network

The functional meaning and neural basis of the P3b component of ERPs are still under debate. One of the main issues is whether P3b reflects only stimulus-related processes (stimulus evaluation hypothesis) or response-related processes as well (stimulus-response or S-R link activation hypothesis). Here, we conducted an EEG experiment examining whether P3b may indeed reflect an S-R link activation, followed by an fMRI experiment in which we explored the brain areas and functional connectivity possibly constituting the neural basis of these sensorimotor links. In both experiments, two successive visual stimuli, S1 and S2, were presented with a 1 sec interval, and responses were defined either by S1 or S2, while participants responded only after S2 onset. The obtained EEG results suggest that P3b may be interpreted in terms of the S-R link activation account, although further studies are needed to disentangle P3-related activity from overlapping anticipatory activity. The obtained fMRI results showed that processing of the relevant S1 involved activation of a distributed postero-anterior sensorimotor network, and increased strength of functional connectivity within this network. This network may underlie activation of the S-R links, thus possibly also the P3b component, forming a bridging step between sensory encoding and response execution.

Disrupted Brain Structural Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment With No Dementia

The alteration of the functional topological organization in subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) patients has been illuminated by previous neuroimaging studies. However, in regard to the changes in the structural connectivity of brain networks, little has been reported. In this study, a total of 27 subjects, consisting of 13 SIVCIND patients, and 14 normal controls, were recruited. Each of the structural connectivity networks was constructed by diffusion tensor tractography. Subsequently, graph theory, and network-based statistics (NBS) were employed to analyze the whole-brain mean factional anisotropy matrix. After removing the factor of age, gender, and duration of formal education, the clustering coefficients (C p ) and global efficiency (E glob ) were significantly decreased and the mean path length (L p ) was significantly increased in SIVCIND patients compared with normal controls. Using the combination of four network topological parameters as the classification feature, a classification accuracy of 78% was obtained by leave-one-out cross-validation for all subjects with a sensitivity of 69% and a specificity of 86%. Moreover, we also found decreased structural connections in the SIVCIND patients, which mainly concerned fronto-occipital, fronto-subcortical, and tempo-occipital connections (NBS corrected, p < 0.01). Additionally, significantly altered nodal centralities were found in several brain regions of the SIVCIND patients, mainly located in the prefrontal, subcortical, and temporal cortices. These results suggest that cognitive impairment in SIVCIND patients is associated with disrupted topological organization and provide structural evidence for developing reliable biomarkers related to cognitive decline in SIVCIND.

White matter dissection and structural connectivity of the human vertical occipital fasciculus to link vision-associated brain cortex

The vertical occipital fasciculus (VOF) is an association fiber tract coursing vertically at the posterolateral corner of the brain. It is re-evaluated as a major fiber tract to link the dorsal and ventral visual stream. Although previous tractography studies showed the VOF’s cortical projections fall in the dorsal and ventral visual areas, the post-mortem dissection study for the validation remains limited. First, to validate the previous tractography data, we here performed the white matter dissection in post-mortem brains and demonstrated the VOF’s fiber bundles coursing between the V3A/B areas and the posterior fusiform gyrus. Secondly, we analyzed the VOF’s structural connectivity with diffusion tractography to link vision-associated cortical areas of the HCP MMP1.0 atlas, an updated map of the human cerebral cortex. Based on the criteria the VOF courses laterally to the inferior longitudinal fasciculus (ILF) and craniocaudally at the posterolateral corner of the brain, we reconstructed the VOF’s fiber tracts and found the widespread projections to the visual cortex. These findings could suggest a crucial role of VOF in integrating visual information to link the broad visual cortex as well as in connecting the dual visual stream.

The effect of network thresholding and weighting on structural brain networks in the UK Biobank

Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ​≤ ​|β| ​≤ ​0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ​≤ ​|β| ​≤ ​0.406) than the consistency-based approach which retained only 30% of connections (0.140 ​≤ ​|β| ​≤ ​0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC ​= ​0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ​≤ ​|0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ​≤ ​|0.219|, p ​< ​0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.

Early Life Adversities and Borderline Intellectual Functioning Negatively Impact Limbic System Connectivity in Childhood: A Connectomics-Based Study

Early life adversity (ELA) in childhood is a major risk factor for borderline intellectual functioning (BIF). BIF affects both adaptive and intellectual abilities, commonly leading to school failure and to an increased risk to develop mental and social problems in the adulthood. This study aimed to investigate the neurobiological underpinnings of ELA associated with BIF in terms of global topological organization and structural connectivity and their relation with intellectual functioning. BIF (N=32) and age-matched typical development (TD, N=14) children were evaluated for intelligence quotient (IQ), behavioral competencies, and ELA. Children underwent an anatomical and diffusion-weighted MR imaging (DWI) protocol. Global brain topological organization was assessed measuring segregation and integration indexes. Moreover, structural matrices, measuring normalized number of fibers (NFn), were compared between the 2 groups using network-based statistics. Finally, a linear regression model was used to explore the relationship between network parameters and clinical measures. Results showed increased behavioral difficulties and ELA, together with decreased network integration in BIF children. Moreover, significantly lower NFn was observed in the BIF group (p=.039) in a sub-network comprising anterior and posterior cingulate, the pericallosal sulcus, the orbital frontal areas, amygdala, basal ganglia, the accumbens nucleus, and the hippocampus. Linear regression showed that NFn significantly predicted IQ (p<.0001). This study demonstrated that ELA in children with BIF is associated with a decreased information integration at the global level, and with an altered structural connectivity within the limbic system strictly related to the intellectual functioning.

A Distance-Based Neurorehabilitation Evaluation Method Using Linear SVM and Resting-State fMRI

During neurorehabilitation, clinical measurements are widely adopted to evaluate behavioral improvements after treatment. However, it is not able to identify or monitor the change of central nervous system (CNS) of each individual patient. Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to investigate brain functions in healthy controls (HCs) and patients with neurological diseases, which could find functional changes following neurorehabilitation. In this paper, a distance-based rehabilitation evaluation method based on rs-fMRI was proposed. Specifically, we posit that in the functional connectivity (FC) space, patients and HCs distribute separately. Linear support vector machines (SVM) were trained on the brain networks to firstly separate patients from HCs. Second, the FC similarity between patients and HCs was measured by the L2 distance of each subject's feature vector to the separating hyperplane. Finally, statistical analysis of the distance revealed rehabilitation program induced improvements in patients and predicted rehabilitation outcomes. An rs-fMRI dataset with 22 HCs and 18 spinal cord injury (SCI) patients was utilized to validate our method. We built whole-brain networks using five atlases to test the robustness of the method and search for features under different node resolutions. The classifier successfully separated patients and HCs. Significant improvements in FC after treatment were found for the patients for all five atlases using the proposed method, which was consistent with clinical measurements. Furthermore, distance obtained from individual patient's longitudinal data showed a similar trend with each one's clinical scores, implying the possibility of individual rehabilitation outcome tracking and prediction. Our method not only provides a novel perspective of applying rs-fMRI to neurorehabilitation monitoring but also proves the potential in individualized rehabilitation prediction.

History of early life adversity is associated with increased food addiction and sex-specific alterations in reward network connectivity in obesity

BACKGROUND

Neuroimaging studies have identified obesity-related differences in the brain's resting state activity. An imbalance between homeostatic and reward aspects of ingestive behaviour may contribute to obesity and food addiction. The interactions between early life adversity (ELA), the reward network and food addiction were investigated to identify obesity and sex-related differences, which may drive obesity and food addiction.

METHODS

Functional resting state magnetic resonance imaging was acquired in 186 participants (high body mass index [BMI]: ≥25: 53 women and 54 men; normal BMI: 18.50-24.99: 49 women and 30 men). Participants completed questionnaires to assess ELA (Early Traumatic Inventory) and food addiction (Yale Food Addiction Scale). A tripartite network analysis based on graph theory was used to investigate the interaction between ELA, brain connectivity and food addiction. Interactions were determined by computing Spearman rank correlations, thresholded at q < 0.05 corrected for multiple comparisons.

RESULTS

Participants with high BMI demonstrate an association between ELA and food addiction, with reward regions playing a role in this interaction. Among women with high BMI, increased ELA was associated with increased centrality of reward and emotion regulation regions. Men with high BMI showed associations between ELA and food addiction with somatosensory regions playing a role in this interaction.

CONCLUSIONS

The findings suggest that ELA may alter brain networks, leading to increased vulnerability for food addiction and obesity later in life. These alterations are sex specific and involve brain regions influenced by dopaminergic or serotonergic signalling.

Impact of early adverse life events and sex on functional brain networks in patients with urological chronic pelvic pain syndrome (UCPPS): A MAPP Research Network study

Pain is a highly complex and individualized experience with biopsychosocial components. Neuroimaging research has shown evidence of the involvement of the central nervous system in the development and maintenance of chronic pain conditions, including urological chronic pelvic pain syndrome (UCPPS). Furthermore, a history of early adverse life events (EALs) has been shown to adversely impact symptoms throughout childhood and into adulthood. However, to date, the role of EAL's in the central processes of chronic pain have not been adequately investigated. We studied 85 patients (56 females) with UCPPS along with 86 healthy controls (HCs) who had resting-state magnetic resonance imaging scans (59 females), and data on EALs as a part of the Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) Research Network Study. We used graph theory methods in order to investigate the impact of EALs on measures of centrality, which characterize information flow, communication, influence, and integration in a priori selected regions of interest. Patients with UCPPS exhibited lower centrality in the right anterior insula compared to HCs, a key node in the salience network. Males with UCPPS exhibited lower centrality in the right anterior insula compared the HC males. Females with UCPPS exhibited greater centrality in the right caudate nucleus and left angular gyrus compared to HC females. Males with UCPPS exhibited lower centrality in the left posterior cingulate, angular gyrus, middle temporal gyrus, and superior temporal sulcus, but greater centrality in the precuneus and anterior mid-cingulate cortex (aMCC) compared to females with UCPPS. Higher reports of EALs was associated with greater centrality in the left precuneus and left aMCC in females with UCPPS. This study provides evidence for disease and sex-related alterations in the default mode, salience, and basal ganglia networks in patients with UCPPS, which are moderated by EALs, and associated with clinical symptoms and quality of life (QoL).

Evidence for an association of gut microbial Clostridia with brain functional connectivity and gastrointestinal sensorimotor function in patients with irritable bowel syndrome, based on tripartite network analysis

BACKGROUND AND AIMS

Evidence from preclinical and clinical studies suggests that interactions among the brain, gut, and microbiota may affect the pathophysiology of irritable bowel syndrome (IBS). As disruptions in central and peripheral serotonergic signaling pathways have been found in patients with IBS, we explored the hypothesis that the abundance of serotonin-modulating microbes of the order Clostridiales is associated with functional connectivity of somatosensory brain regions and gastrointestinal (GI) sensorimotor function.

METHODS

We performed a prospective study of 65 patients with IBS and 21 healthy individuals (controls) recruited from 2011 through 2013 at a secondary/tertiary care outpatient clinic in Sweden. Study participants underwent functional brain imaging, rectal balloon distension, a nutrient and lactulose challenge test, and assessment of oroanal transit time within a month. They also submitted stool samples, which were analyzed by 16S ribosomal RNA gene sequencing. A tripartite network analysis based on graph theory was used to investigate the interactions among bacteria in the order Clostridiales, connectivity of brain regions in the somatosensory network, and GI sensorimotor function.

RESULTS

We found associations between GI sensorimotor function and gut microbes in stool samples from controls, but not in samples from IBS patients. The largest differences between controls and patients with IBS were observed in the Lachnospiraceae incertae sedis, Clostridium XIVa, and Coprococcus subnetworks. We found connectivity of subcortical (thalamus, caudate, and putamen) and cortical (primary and secondary somatosensory cortices) regions to be involved in mediating interactions among these networks.

CONCLUSIONS

In a comparison of patients with IBS and controls, we observed disruptions in the interactions between the brain, gut, and gut microbial metabolites in patients with IBS-these involve mainly subcortical but also cortical regions of brain. These disruptions may contribute to altered perception of pain in patients with IBS and may be mediated by microbial modulation of the gut serotonergic system.

Big data sharing and analysis to advance research in post-traumatic epilepsy

We describe the infrastructure and functionality for a centralized preclinical and clinical data repository and analytic platform to support importing heterogeneous multi-modal data, automatically and manually linking data across modalities and sites, and searching content. We have developed and applied innovative image and electrophysiology processing methods to identify candidate biomarkers from MRI, EEG, and multi-modal data. Based on heterogeneous biomarkers, we present novel analytic tools designed to study epileptogenesis in animal model and human with the goal of tracking the probability of developing epilepsy over time.

Patients with sickle-cell disease exhibit greater functional connectivity and centrality in the locus coeruleus compared to anemic controls

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Patients with sickle-cell disease (SCD) have greater resting-state functional connectivity between the locus coeruleus (LC) and dorsolateral prefrontal cortex (dlPFC).

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Patients with SCD have greater resting state centrality of the LC

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SCD patients with chronic pain exhibited even greater functional connectivity between the LC and dlPFC.

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This study supports hyper-connectivity between the LC and PFC is a potential chronic pain generator.

Support Vector Machines, Multidimensional Scaling and Magnetic Resonance Imaging Reveal Structural Brain Abnormalities Associated With the Interaction Between Autism Spectrum Disorder and Sex

Despite substantial efforts, it remains difficult to identify reliable neuroanatomic biomarkers of autism spectrum disorder (ASD) based on magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). Studies which use standard statistical methods to approach this task have been hampered by numerous challenges, many of which are innate to the mathematical formulation and assumptions of general linear models (GLM). Although the potential of alternative approaches such as machine learning (ML) to identify robust neuroanatomic correlates of psychiatric disease has long been acknowledged, few studies have attempted to evaluate the abilities of ML to identify structural brain abnormalities associated with ASD. Here we use a sample of 110 ASD patients and 83 typically developing (TD) volunteers (95 females) to assess the suitability of support vector machines (SVMs, a robust type of ML) as an alternative to standard statistical inference for identifying structural brain features which can reliably distinguish ASD patients from TD subjects of either sex, thereby facilitating the study of the interaction between ASD diagnosis and sex. We find that SVMs can perform these tasks with high accuracy and that the neuroanatomic correlates of ASD identified using SVMs overlap substantially with those found using conventional statistical methods. Our results confirm and establish SVMs as powerful ML tools for the study of ASD-related structural brain abnormalities. Additionally, they provide novel insights into the volumetric, morphometric, and connectomic correlates of this epidemiologically significant disorder.

BRANT: A Versatile and Extendable Resting-State fMRI Toolkit

Data processing toolboxes for resting-state functional MRI (rs-fMRI) have provided us with a variety of functions and user friendly graphic user interfaces (GUIs). However, many toolboxes only cover a certain range of functions, and use exclusively designed GUIs. To facilitate data processing and alleviate the burden of manually drawing GUIs, we have developed a versatile and extendable MATLAB-based toolbox, BRANT (BRAinNetome fmri Toolkit), with a wide range of rs-fMRI data processing functions and code-generated GUIs. During the implementation, we have also empowered the toolbox with parallel computing techniques, efficient file handling methods for compressed file format, and one-line scripting. In BRANT, users can find rs-fMRI batch processing functions for preprocessing, brain spontaneous activity analysis, functional connectivity analysis, complex network analysis, statistical analysis, and results visualization, while developers can quickly publish scripts with code-generated GUIs.

An Uncertainty Visual Analytics Framework for fMRI Functional Connectivity

Analysis and interpretation of functional magnetic resonance imaging (fMRI) has been used to characterise many neuronal diseases, such as schizophrenia, bipolar disorder and Alzheimer's disease. Functional connectivity networks (FCNs) are widely used because they greatly reduce the amount of data that needs to be interpreted and they provide a common network structure that can be directly compared. However, FCNs contain a range of data uncertainties stemming from inherent limitations, e.g. during acquisition, as well as the loss of voxel-level data, and the use of thresholding in data abstraction. Additionally, human uncertainties arise during interpretation due to the complexity in understanding the data. While existing FCN visual analytics tools have begun to mitigate the human ambiguities, reducing the impact of data limitations is an open problem. In this paper, we propose a novel visual analytics framework with three linked, purpose-designed components to evoke deeper interpretation of the fMRI data: (i) an enhanced FCN abstraction; (ii) a temporal signal viewer; and (iii) the anatomical context. Each component has been specifically designed with novel visual cues and interaction to expose the impact of uncertainties on the data. We augment this with two methods designed for comparing subjects, by using a small multiples and a marker approach. We demonstrate the enhancements enabled by our framework on three case studies of common research scenarios, using clinical schizophrenia data, which highlight the value in interpreting fMRI FCN data with an awareness of the uncertainties. Finally, we discuss our framework in the context of fMRI visual analytics and the extensibility of our approach.

Correlation of tryptophan metabolites with connectivity of extended central reward network in healthy subjects

OBJECTIVE

A growing body of preclinical and clinical literature suggests that brain-gut-microbiota interactions play an important role in human health and disease, including hedonic food intake and obesity. We performed a tripartite network analysis based on graph theory to test the hypothesis that microbiota-derived fecal metabolites are associated with connectivity of key regions of the brain's extended reward network and clinical measures related to obesity.

METHODS

DTI and resting state fMRI imaging was obtained from 63 healthy subjects with and without elevated body mass index (BMI) (29 males and 34 females). Subjects submitted fecal samples, completed questionnaires to assess anxiety and food addiction, and BMI was recorded.

RESULTS

The study results demonstrate associations between fecal microbiota-derived indole metabolites (indole, indoleacetic acid, and skatole) with measures of functional and anatomical connectivity of the amygdala, nucleus accumbens, and anterior insula, in addition to BMI, food addiction scores (YFAS) and anxiety symptom scores (HAD Anxiety).

CONCLUSIONS

The findings support the hypothesis that gut microbiota-derived indole metabolites may influence hedonic food intake and obesity by acting on the extended reward network, specifically the amygdala-nucleus accumbens circuit and the amygdala-anterior insula circuit. These cross sectional, data-driven results provide valuable information for future mechanistic studies.

A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging

Analysis of functional magnetic resonance imaging (fMRI) plays a pivotal role in uncovering an understanding of the brain. fMRI data contain both spatial volume and temporal signal information, which provide a depiction of brain activity. The analysis pipeline, however, is hampered by numerous uncertainties in many of the steps; often seen as one of the last hurdles for the domain. In this review, we categorise fMRI research into three pipeline phases: (i) image acquisition and processing; (ii) image analysis; and (iii) visualisation and human interpretation, to explore the uncertainties that arise in each phase, including the compound effects due to the inter-dependence of steps. Attempts at mitigating uncertainties rely on providing interactive visual analytics that aid users in understanding the effects of the uncertainties and adjusting their analyses. This impetus for visual analytics comes in light of considerable research investigating uncertainty throughout the pipeline. However, to the best of our knowledge, there is yet to be a comprehensive review on the importance and utility of uncertainty visual analytics (UVA) in addressing fMRI concerns, which we term fMRI-UVA. Such techniques have been broadly implemented in related biomedical fields, and its potential for fMRI has recently been explored; however, these attempts are limited in their scope and utility, primarily focussing on addressing small parts of single pipeline phases. Our comprehensive review of the fMRI uncertainties from the perspective of visual analytics addresses the three identified phases in the pipeline. We also discuss the two interrelated approaches for future research opportunities for fMRI-UVA.

Connectivity in fMRI: Blind Spots and Breakthroughs

In recent years, driven by scientific and clinical concerns, there has been an increased interest in the analysis of functional brain networks. The goal of these analyses is to better understand how brain regions interact, how this depends upon experimental conditions and behavioral measures and how anomalies (disease) can be recognized. In this paper, we provide, first, a brief review of some of the main existing methods of functional brain network analysis. But rather than compare them, as a traditional review would do, instead, we draw attention to their significant limitations and blind spots. Then, second, relevant experts, sketch a number of emerging methods, which can break through these limitations. In particular we discuss five such methods. The first two, stochastic block models and exponential random graph models, provide an inferential basis for network analysis lacking in the exploratory graph analysis methods. The other three addresses: network comparison via persistent homology, time-varying connectivity that distinguishes sample fluctuations from neural fluctuations, and network system identification that draws inferential strength from temporal autocorrelation.

Sigmund Freud-early network theories of the brain

Since the early days of modern neuroscience, psychological models of brain function have been a key component in the development of new knowledge. These models aim to provide a framework that allows the integration of discoveries derived from the fundamental disciplines of neuroscience, including anatomy and physiology, as well as clinical neurology and psychiatry. During the initial stages of his career, Sigmund Freud (1856-1939), became actively involved in these nascent fields with a burgeoning interest in functional neuroanatomy. In contrast to his contemporaries, Freud was convinced that cognition could not be localised to separate modules and that the brain processes cognition not in a merely serial manner but in a parallel and dynamic fashion-anticipating fundamental aspects of current network theories of brain function. This article aims to shed light on Freud's seminal, yet oft-overlooked, early work on functional neuroanatomy and his reasons for finally abandoning the conventional neuroscientific "brain-based" reference frame in order to conceptualise the mind from a purely psychological perspective.

Progressively Disrupted Brain Functional Connectivity Network in Subcortical Ischemic Vascular Cognitive Impairment Patients

Cognitive impairment caused by subcortical ischemic vascular disease (SIVD) has been elucidated by many neuroimaging studies. However, little is known regarding the changes in brain functional connectivity networks in relation to the severity of cognitive impairment in SIVD. In the present study, 20 subcortical ischemic vascular cognitive impairment no dementia patients (SIVCIND) and 20 dementia patients (SIVaD) were enrolled; additionally, 19 normal controls were recruited. Each participant underwent a resting-state functional MRI scan. Whole-brain functional networks were analyzed with graph theory and network-based statistics (NBS) to study the functional organization of networks and find alterations in functional connectivity among brain regions. After adjustments for age, gender, and duration of formal education, there were significant group differences for two network functional organization indices, global efficiency and local efficiency, which decreased (NC > SIVCIND > SIVaD) as cognitive impairment worsened. Between-group differences in functional connectivity (NBS corrected, p < 0.01) mainly involved the orbitofrontal, parietal, and temporal cortices, as well as the basal ganglia. The brain connectivity network was progressively disrupted as cognitive impairment worsened, with an increased number of decreased connections between brain regions. We also observed more reductions in nodal efficiency in the prefrontal and temporal cortices for SIVaD than for SIVCIND. These findings indicated a progressively disrupted pattern of the brain functional connectivity network with increased cognitive impairment and showed promise for the development of reliable biomarkers of network metric changes related to cognitive impairment caused by SIVD.