Choice of Voxel-based Morphometry processing pipeline drives variability in the location of neuroanatomical brain markers

Post mortem cadaveric and imaging mapping analysis of the influence of cochlear implants on cMRI assessment regarding implant positioning and artifact formation

OBJECTIVES

In times of an aging society and considering the escalating health economic costs, the indications for imaging, particularly magnetic resonance imaging (MRI), must be carefully considered and strictly adhered to. This cadaver study aims to examine the influence of cochlear implant (CI) on the assessment of intracranial structures, artifact formation, and size in cranial MRI (cMRI). Furthermore, it seeks to evaluate the potential limitations in the interpretability and diagnostic value of cMRI in CI patients. Additionally, the study investigates the imaging of the brain stem and the internal ear canal and the feasibility of excluding cholesteatomas in cMRI for CI patients.

MATERIALS AND METHODS

Two cadaveric specimens were implanted with cochlear implants at varying angular positions (90°, 120°, and 135°), both unilaterally and bilaterally, with and without magnet in situ. MRI acquisition consisted of sequences commonly used in brain MRI scans (T1-MP-RAGE, T2-TSE, T1-TIRM, DWI, CISS). Subsequently, the obtained MRI images were manually juxtaposed with a reference brain from the Computational Anatomy Toolbox CAT12. The size and formation of artifacts were scrutinized to ascertain the assessability of 22 predefined intracranial structures. Furthermore, the internal auditory canal, middle ear and mastoid were evaluated.

RESULTS

The cadaveric head mapping facilitated the analysis of all 22 predefined intracranial structures. Artifacts were assessed in terms of their minimum and maximum impact on image comparability. Image quality and assessability were stratified into four categories (0-25%, 25-50%, 50-75%, and 75-100% of assessability restriction). The visualization of the central, temporal, parietal, and frontal lobes was contingent upon CI positioning and the choice of imaging sequence. Diffusion-weighted cMRI proved inadequate for monitoring cholesteatoma recurrence in ipsilateral CI patients, regardless of magnet presence. The ipsilateral internal auditory canal was inadequately visualized in both magnet-present and magnet-absent conditions. We divided our results into four categories. Category 3 (orange) indicates considerable limitations, while category 4 (red) indicates no interpretability, as the image is entirely obscured by artifacts.

CONCLUSION

This study provides detailed predictive power for the assessability and therefore the relevance of performing cMRIs in CI patients. We advocate consulting the relevant CI center if artifact overlay exceeds 50% (categories 3 and 4), to evaluate magnet explantation and reassess the necessity of cMRI. When suspecting cholesteatoma or cholesteatoma recurrences in patients with ipsilateral cochlear implants, diagnostic investigation should preferably be pursued surgically, as the necessary MRI sequences are prone to artifact interference, even in the absence of a magnet. The ipsilateral internal auditory canal remains inadequately evaluable with a magnet in situ, while without the magnet, only rudimentary assessments can be made across most sequences.

Multiclass classification of Alzheimer's disease prodromal stages using sequential feature embeddings and regularized multikernel support vector machine

The detection of patients in the cognitive normal (CN), mild cognitive impairment (MCI), and Alzheimer's disease (AD) stages of neurodegeneration is crucial for early treatment interventions. However, the heterogeneity of MCI data samples poses a challenge for CN vs. MCI vs. AD multiclass classification, as some samples are closer to AD while others are closer to CN in the feature space. Previous attempts to address this challenge produced inaccurate results, leading most frameworks to break the assessment into binary classification tasks such as AD vs. CN, AD vs. MCI, and CN vs. MCI. Other methods proposed sequential binary classifications such as CN vs. others and dividing others into AD vs. MCI. While those approaches may have yielded encouraging results, the sequential binary classification method makes interpretation and comparison with other frameworks challenging and subjective. Those frameworks exhibited varying accuracy scores for different binary tasks, making it unclear how to compare the model performance with other direct multiclass methods. Therefore, we introduce a classification framework comprising unsupervised ensemble manifold regularized sparse low-rank approximation and regularized multikernel support vector machine (SVM). This framework first extracts a joint feature embedding from MRI and PET neuroimaging features, which were then combined with the Apoe4, Adas11, MPACC digits, and Intracranial volume features using a regularized multikernel SVM. Using that framework, we achieved a state-of-the-art (SOTA) result in a CN vs. MCI vs. AD multiclass classification (mean accuracy: 84.87±6.09, F1 score: 84.83±6.12 vs 67.69). The methods generalize well to binary classification tasks, achieving SOTA results in all but the CN vs. MCI category, which was slightly lower than the best score by just 0.2%.

Hypothalamic atrophy and structural covariance in amnestic mild cognitive impairment and Alzheimer's dementia

BACKGROUND

Alzheimer's disease (AD) is characterized by progressive cognitive decline and specific brain atrophy patterns, primarily involving the medial temporal lobes. A number of studies have discussed hypothalamic involvement in AD with consecutive metabolic and/or autonomic disturbances yet only few studies have investigated hypothalamic atrophy in AD and its early stages in particular.

METHODS

We applied semi-automated volumetry of the hypothalamus (HTH) in 3 T MRI in a sample N = 175 participants [age 74.9 ± 7.22; gender 85 m/90f; cognitively normal controls (CN; N = 56); amnestic mild cognitive impairment (MCI; N = 78); AD (N = 41)] from the Alzheimer's Disease Neuroimaging Initiative (ADNI). In addition, we used voxel-based morphometry (VBM), cortical thickness (CTH) analyses and source-based morphometry (SBM) derived networks of structural covariance to investigate brain structural covariance patterns of the HTH under consideration of diagnostic groups, β-amyloid (AB) positivity and apolipoprotein E (APOE) ε4 status.

RESULTS

Hypothalamic atrophy was observed in both early and advanced disease stages (i.e. hypothalamic volume CN > MCI > AD). VBM, CTH analysis and SBM revealed positive associations between hypothalamic volume (HV) and AD-vulnerable regions, largely corresponding to the Papez circuit and brain regions implicated in autonomic regulation, however, group differences regarding HTH structural covariance were not observed. Similar observations were made in carriers and non-carriers of the ε4 allele, yet more pronounced in ε4 carriers. Although not reaching significance, comparisons of AB positive vs. negative subjects indicated stronger HTH atrophy in biomarker positive participants. HV was not associated with body mass index or longitudinal weight change.

CONCLUSIONS

Our findings support early structural changes of the HTH in AD. HV covaries with regional volumes of AD-vulnerable regions. This could point to secondary atrophy of the HTH following atrophy of the hippocampus and other structures of the Papez circuit in AD.

Neural Basis of Pain Empathy Dysregulations in Mental Disorders: A Preregistered Neuroimaging Meta-Analysis

BACKGROUND

Pain empathy represents a fundamental building block of several social functions, which have been demonstrated to be impaired across various mental disorders by accumulating evidence from case-control functional magnetic resonance imaging studies. However, it remains unclear whether the dysregulations are underpinned by robust neural alterations across mental disorders.

METHODS

This study utilized coordinate-based meta-analyses to quantitatively determine robust markers of altered pain empathy across mental disorders. To support the interpretation of the findings, exploratory network-level and behavioral meta-analyses were conducted.

RESULTS

Quantitative analysis of 11 case-control functional magnetic resonance imaging studies with data from 296 patients and 229 control participants revealed that patients with mental disorders exhibited increased pain empathic reactivity in the left anterior cingulate gyrus, adjacent medial prefrontal cortex, and right middle temporal gyrus but decreased activity in the left cerebellum IV/V and left middle occipital gyrus compared with control participants. The hyperactive regions showed network-level interactions with the core default mode network and were associated with affective and social cognitive domains.

CONCLUSIONS

The findings suggest that pain empathic alterations across mental disorders are underpinned by excessive empathic reactivity in brain systems involved in empathic distress and social processes, highlighting a shared therapeutic target to normalize basal social dysfunctions in mental disorders.

Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction

Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting the complementarity between multi-modal data. This omission may lead to poor classification. Therefore, it is important to study multi-modal data of ASD for revealing its pathogenesis. Furthermore, recurrent neural network (RNN) and gated recurrent unit (GRU) are effective for sequence data processing. In this paper, we introduce a novel framework for a Multi-Kernel Learning Fusion algorithm based on RNN and GRU (MKLF-RAG). The framework utilizes RNN and GRU to provide feature selection for data of different modalities. Then these features are fused by MKLF algorithm to detect the pathological mechanisms of ASD and extract the most relevant the Regions of Interest (ROIs) for the disease. The MKLF-RAG proposed in this paper has been tested in a variety of experiments with the Autism Brain Imaging Data Exchange (ABIDE) database. Experimental findings indicate that our framework notably enhances the classification accuracy for ASD. Compared with other methods, MKLF-RAG demonstrates superior efficacy across multiple evaluation metrics and could provide valuable insights into the early diagnosis of ASD.

Pseudo-time Series Structural MRI Revealing Progressive Gray Matter Changes with Elevated Intraocular Pressure in Primary Open-Angle Glaucoma: A Preliminary Study

RATIONALE AND OBJECTIVES

Primary open-angle glaucoma (POAG) is accompanied with gray matter (GM) changes across the brain. However, causal relationships of the GM changes have not been fully understood. Our aim was to investigate the causality of GM progressive changes in POAG using Granger causality (GC) analysis and structural MRI.

MATERIALS AND METHODS

Structural MRI from 20 healthy controls and 30 POAG patients with elevated intraocular pressure (IOP) were collected. We performed voxel-wise GM volume comparisons between control and POAG groups, and between control and four POAG subgroups (categorized by IOP). Then, we sequenced the structural MRI data of all POAG patients and conducted both voxel-wise and region of interest (ROI)-wise GC analysis to investigate the causality of GM volume changes in POAG brain.

RESULTS

Compared to healthy controls, reduced GM volumes across the brain were found, GM volume enlargements in the thalamus, caudate nucleus and cuneus were also observed in POAG brain (false discovery rate (FDR) corrected at q< 0.05). As IOP elevated, the reductions of GM volume were more severe in the cerebellum and frontal lobe. GC analysis revealed that the bilateral cerebellum, visual cortices, and the frontal regions served independently as primary hubs of the directional causal network, and projected causal effects to the parietal and temporal regions of the brain (FDR corrected at q<0.05).

CONCLUSION

POAG exhibits progressive GM alterations across the brain, with oculomotor regions and visual cortices as independent primary hubs. The current results may deepen our understanding of neuropathology of POAG.

Common and separable neural alterations in adult and adolescent depression - Evidence from neuroimaging meta-analyses

Depression is a highly prevalent and debilitating mental disorder that often begins in adolescence. However, it remains unclear whether adults and adolescents with depression exhibit common or distinct brain dysfunctions during reward processing. We aimed to identify common and separable neurofunctional alterations during receipt of rewards and brain structure in adolescents and adults with depression. A coordinate-based meta-analysis was employed using Seed-based d mapping with permutation of subject images (SDM-PSI). Compared with healthy controls, both age groups exhibited common activity decreases in the right striatum (putamen, caudate) and subgenual ACC. Adults with depression showed decreased reactivity in the right putamen and subgenual ACC, while adolescents with depression showed decreased activity in the left mid cingulate, right caudate but increased reactivity in the right postcentral gyrus. This meta-analysis revealed shared (caudate) and separable (putamen and mid cingulate cortex) reward-related alterations in adults and adolescents with depression. The findings suggest age-specific neurofunctional alterations and stress the importance of adolescent-specific interventions that target social functions.

The neurostructural consequences of glaucoma and their overlap with disorders exhibiting emotional dysregulations: A voxel-based meta-analysis and tripartite system model

BACKGROUND

Glaucoma, a progressive neurodegenerative disorder leading to irreversible blindness, is associated with heightened rates of generalized anxiety and depression. This study aims to comprehensively investigate brain morphological changes in glaucoma patients, extending beyond visual processing areas, and explores overlaps with morphological alterations observed in anxiety and depression.

METHODS

A comparative meta-analysis was conducted, using case-control studies of brain structural integrity in glaucoma patients. We aimed to identify regions with gray matter volume (GMV) changes, examine their role within distinct large-scale networks, and assess overlap with alterations in generalized anxiety disorder (GAD) and major depressive disorder (MDD).

RESULTS

Glaucoma patients exhibited significant GMV reductions in visual processing regions (lingual gyrus, thalamus). Notably, volumetric reductions extended beyond visual systems, encompassing the left putamen and insula. Behavioral and functional network decoding revealed distinct large-scale networks, implicating visual, motivational, and affective domains. The insular region, linked to pain and affective processes, displayed reductions overlapping with alterations observed in GAD.

LIMITATIONS

While the study identified significant morphological alterations, the number of studies from both the glaucoma and GAD cohorts remains limited due to the lack of independent studies meeting our inclusion criteria.

CONCLUSION

The study proposes a tripartite brain model for glaucoma, with visual processing changes related to the lingual gyrus and additional alterations in the putamen and insular regions tied to emotional or motivational functions. These neuroanatomical changes extend beyond the visual system, implying broader implications for brain structure and potential pathological developments, providing insights into the overall neurological consequences of glaucoma.

The subcortical basis of subjective sleep quality

Study objectives

To assess the association between self-reported sleep quality and cortical and subcortical local morphometry.

Methods

Sleep and neuroanatomical data from the full release of the young adult Human Connectome Project dataset were analyzed. Sleep quality was operationalized with the Pittsburgh Sleep Quality Index (PSQI). Local cortical and subcortical morphometry was measured with subject-specific segmentations resulting in voxelwise thickness measurements for cortex and relative (i.e., cross-sectional) local atrophy measurements for subcortical regions.

Results

Relative atrophy across several subcortical regions, including bilateral pallidum, striatum, and thalamus, was negatively associated with both global PSQI score and sub-components of the index related to sleep duration, efficiency, and quality. Conversely, we found no association between cortical morphometric measurements and self-reported sleep quality.

Conclusions

This work shows that subcortical regions such as the bilateral pallidum, thalamus, and striatum, might be interventional targets to ameliorate self-reported sleep quality.

Gray Matter Adaptations to Chronic Pain in People with Whiplash-Associated Disorders are Partially Reversed After Treatment: A Voxel-based Morphometry Study

Gray matter (GM) changes are often observed in people with chronic spinal pain, including those with chronic whiplash-associated disorders (CWAD). These GM adaptations may be reversed with treatment, at least partially. Pain neuroscience education combined with exercise (PNE+Exercise) is an effective treatment, but its neural underlying mechanisms still remain unexplored in CWAD. Here, we performed both cross-sectional and longitudinal voxel-based morphometry to 1) identify potential GM alterations in people with CWAD (n = 63) compared to age- and sex-matched pain-free controls (n = 32), and 2) determine whether these GM alterations might be reversed following PNE+Exercise (compared to conventional physiotherapy). The cross-sectional whole-brain analysis revealed that individuals with CWAD had less GM volume in the right and left dorsolateral prefrontal cortex and left inferior temporal gyrus which was, in turn, associated with higher pain vigilance. Fifty individuals with CWAD and 29 pain-free controls were retained in the longitudinal analysis. GM in the right dorsolateral prefrontal cortex increased after treatment in people with CWAD. Moreover, the longitudinal whole-brain analysis revealed that individuals with CWAD had decreases in GM volumes of the left and right central operculum and supramarginal after treatment. These changes were not specific to treatment modality and some were not observed in pain-free controls over time. Herewith, we provide the first evidence on how GM adaptations to CWAD respond to treatment. PERSPECTIVE: This article presents which gray matter adaptations are present in people with chronic pain after whiplash injuries. Then, we examine the treatment effect on these alterations as well as whether other neuroplastic effects on GM following treatment occur.

Computational limits to the legibility of the imaged human brain

Our knowledge of the organisation of the human brain at the population-level is yet to translate into power to predict functional differences at the individual-level, limiting clinical applications and casting doubt on the generalisability of inferred mechanisms. It remains unknown whether the difficulty arises from the absence of individuating biological patterns within the brain, or from limited power to access them with the models and compute at our disposal. Here we comprehensively investigate the resolvability of such patterns with data and compute at unprecedented scale. Across 23 810 unique participants from UK Biobank, we systematically evaluate the predictability of 25 individual biological characteristics, from all available combinations of structural and functional neuroimaging data. Over 4526 GPU*hours of computation, we train, optimize, and evaluate out-of-sample 700 individual predictive models, including fully-connected feed-forward neural networks of demographic, psychological, serological, chronic disease, and functional connectivity characteristics, and both uni- and multi-modal 3D convolutional neural network models of macro- and micro-structural brain imaging. We find a marked discrepancy between the high predictability of sex (balanced accuracy 99.7%), age (mean absolute error 2.048 years, R2 0.859), and weight (mean absolute error 2.609Kg, R2 0.625), for which we set new state-of-the-art performance, and the surprisingly low predictability of other characteristics. Neither structural nor functional imaging predicted an individual's psychology better than the coincidence of common chronic disease (p < 0.05). Serology predicted chronic disease (p < 0.05) and was best predicted by it (p < 0.001), followed by structural neuroimaging (p < 0.05). Our findings suggest either more informative imaging or more powerful models will be needed to decipher individual level characteristics from the human brain. We make our models and code openly available.

Impact of APOE-ε alleles on brain structure and cognitive function in healthy older adults: A VBM and DTI replication study

BACKGROUND

The Apolipoprotein E (APOE) gene has been established in the Alzheimer's disease (AD) literature to impact brain structure and function and may also show congruent effects in healthy older adults, although findings in this population are much less consistent. The current study aimed to replicate and expand the multimodal approach employed by Honea et al. Structural magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), and neuropsychological measures were used to investigate the impact of APOE-ε status on grey matter structure, white matter integrity, and cognitive functioning.

METHODS

Data were obtained from the Alzheimer's Disease Initiative Phase 3 (ADNI3) database. Baseline MRI, DTI and cognitive composite scores for memory (ADNI-Mem) and executive function (ADNI-EF) were acquired from 116 healthy controls. Participants were grouped according to APOE allele presence (APOE-ε2+ N = 17, APOE-ε3ε3 N = 64, APOE-ε4+ N = 35). Voxel-based morphometry (VBM) and tract-based spatial statistics (TBSS) were used to compare grey matter volume (GMV) and white matter integrity, respectively, between APOE-ε2+ and APOE-ε3ε3 controls, and again between APOE-ε4+ and APOE-ε3ε3 controls. Multivariate analysis of covariance (MANCOVA) was used to examine the effects of APOE polymorphism on memory and EF across all APOE groups with age, sex and education as regressors of no interest. Cognitive scores were correlated (Pearson r) with imaging metrics within groups.

RESULTS

No significant differences were seen across groups, within groups in MRI metrics, or cognitive performance (p>0.05, corrected for multiple comparisons).

CONCLUSIONS

The current study partially replicated and extended previous findings from an earlier multimodal study (Honea 2009). Future studies should clarify APOE mechanisms in healthy ageing by adding other imaging, cognitive, and lifestyle metrics and longitudinal design in larger sample sizes.

Comparative neuroimaging of sex differences in human and mouse brain anatomy

In vivo neuroimaging studies have established several reproducible volumetric sex differences in the human brain, but the causes of such differences are hard to parse. While mouse models are useful for understanding the cellular and mechanistic bases of sex-specific brain development, there have been no attempts to formally compare human and mouse neuroanatomical sex differences to ascertain how well they translate. Addressing this question would shed critical light on the use of the mouse as a translational model for sex differences in the human brain and provide insights into the degree to which sex differences in brain volume are conserved across mammals. Here, we use structural magnetic resonance imaging to conduct the first comparative neuroimaging study of sex-specific neuroanatomy of the human and mouse brain. In line with previous findings, we observe that in humans, males have significantly larger and more variable total brain volume; these sex differences are not mirrored in mice. After controlling for total brain volume, we observe modest cross-species congruence in the volumetric effect size of sex across 60 homologous regions (r=0.30). This cross-species congruence is greater in the cortex (r=0.33) than non-cortex (r=0.16). By incorporating regional measures of gene expression in both species, we reveal that cortical regions with greater cross-species congruence in volumetric sex differences also show greater cross-species congruence in the expression profile of 2835 homologous genes. This phenomenon differentiates primary sensory regions with high congruence of sex effects and gene expression from limbic cortices where congruence in both these features was weaker between species. These findings help identify aspects of sex-biased brain anatomy present in mice that are retained, lost, or inverted in humans. More broadly, our work provides an empirical basis for targeting mechanistic studies of sex-specific brain development in mice to brain regions that best echo sex-specific brain development in humans.

Severity of white matter microstructural damage in a Brazilian relapsing-remitting multiple sclerosis cohort: A possible window to optimize treatment

BACKGROUND

Multiple sclerosis (MS) is an important cause of acquired neurological disability in young adults, characterized by multicentric inflammation, demyelination, and axonal damage.

OBJECTIVE

The objective is to investigate white matter (WM) damage progression in a Brazilian MS patient cohort, using diffusion tensor imaging (DTI) post-processed by tract-based spatial statistics (TBSS).

METHODS

DTI scans were acquired from 76 MS patients and 37 sex-and-age matched controls. Patients were divided into three groups based on disease duration. DTI was performed along 30 non-collinear directions by using a 1.5T imager. For TBSS analysis, the WM skeleton was created, and a 5000 permutation-based inference with a threshold of p < .05 was used, to enable the identification of abnormalities in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD).

RESULTS

Decreased FA and increased RD, MD, and AD were seen in patients compared to controls and a decreased FA and increased MD and RD were seen, predominantly after the first 5 years of disease, when compared between groups.

CONCLUSION

Progressive WM deterioration is seen over time with a more prominent pattern after 5 years of disease onset, providing evidence that the early years might be a window to optimize treatment and prevent disability.

Gray and White Matter Voxel-Based Morphometry of Alzheimer's Disease With and Without Significant Cerebrovascular Pathologies

Alzheimer's disease (AD) is the most common type of dementia, and AD individuals often present significant cerebrovascular disease (CVD) symptomology. AD with significant levels of CVD is frequently labeled mixed dementia (or sometimes AD-CVD), and the differentiation of these two neuropathologies (AD, AD-CVD) from each other is challenging, especially at early stages. In this study, we compared the gray matter (GM) and white matter (WM) volumes in AD (n = 83) and AD-CVD (n = 37) individuals compared with those of cognitively healthy controls (n = 85) using voxel-based morphometry (VBM) of their MRI scans. The control individuals, matched for age and sex with our two dementia groups, were taken from the ADNI. The VBM analysis showed widespread patterns of significantly lower GM and WM volume in both dementia groups compared to the control group (P < .05, family-wise error corrected). While comparing with AD-CVD, the AD group mainly demonstrated a trend of lower volumes in the GM of the left putamen and right hippocampus and WM of the right thalamus (uncorrected P < .005 with cluster threshold, K = 10). The AD-CVD group relative to AD tended to present lower GM and WM volumes, mainly in the cerebellar lobules and right brainstem regions, respectively (uncorrected P < .005 with cluster threshold, K = 10). Although finding a discriminatory feature in structural MRI data between AD and AD-CVD neuropathologies is challenging, these results provide preliminary evidence that demands further investigation in a larger sample size.

Involvement of the ipsilateral-to-the-pain anterior-superior hypothalamic subunit in chronic cluster headache

BACKGROUND

Despite hypothalamus has long being considered to be involved in the pathophysiology of cluster headache, the inconsistencies of previous neuroimaging studies and a limited understanding of the hypothalamic areas involved, impede a comprehensive interpretation of its involvement in this condition.

METHODS

We used an automated algorithm to extract hypothalamic subunit volumes from 105 cluster headache patients (57 chronic and 48 episodic) and 59 healthy individuals; after correcting the measures for the respective intracranial volumes, we performed the relevant comparisons employing logist regression models. Only for subunits that emerged as abnormal, we calculated their correlation with the years of illness and the number of headache attacks per day, and the effects of lithium treatment. As a post-hoc approach, using the 7 T resting-state fMRI dataset from the Human Connectome Project, we investigated whether the observed abnormal subunit, comprising the paraventricular nucleus and preoptic area, shows robust functional connectivity with the mesocorticolimbic system, which is known to be modulated by oxytocin neurons in the paraventricular nucleus and that is is abnormal in chronic cluster headache patients.

RESULTS

Patients with chronic (but not episodic) cluster headache, compared to control participants, present an increased volume of the anterior-superior hypothalamic subunit ipsilateral to the pain, which, remarkably, also correlates significantly with the number of daily attacks. The post-hoc approach showed that this hypothalamic area presents robust functional connectivity with the mesocorticolimbic system under physiological conditions. No evidence of the effects of lithium treatment on this abnormal subunit was found.

CONCLUSIONS

We identified the ipsilateral-to-the-pain antero-superior subunit, where the paraventricular nucleus and preoptic area are located, as the key hypothalamic region of the pathophysiology of chronic cluster headache. The significant correlation between the volume of this area and the number of daily attacks crucially reinforces this interpretation. The well-known roles of the paraventricular nucleus in coordinating autonomic and neuroendocrine flow in stress adaptation and modulation of trigeminovascular mechanisms offer important insights into the understanding of the pathophysiology of cluster headache.

CAT: a computational anatomy toolbox for the analysis of structural MRI data

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Protocol for a randomized controlled trial exploring the brain mechanism and therapeutic effect of electroacupuncture on cognitive function and sleep quality in chronic insomnia

BACKGROUND

Insomnia is a prevalent sleep disorder that affects up to 15% of the population worldwide and is the second most common mental health issue. There is increasing interest in the effects of long-term insomnia on cognitive function. Electroacupuncture can effectively improve cognitive function and sleep quality, yet the underlying brain network mechanisms remain unclear. This study aims to explore the network regulatory mechanisms associated with enhanced cognitive function and sleep quality, providing theoretical support for the use of electroacupuncture in the clinical treatment of chronic insomnia.

METHODS

This study is divided into two parts. Sixteen individuals with chronic insomnia and 16 healthy controls of similar age and gender will be recruited in Study 1 to examine the brain network topology of individuals with chronic insomnia. Study 2 will be a randomized controlled trial with 120 chronic insomnia patients divided into three groups: Group A (electroacupuncture plus placebo drug), Group B (drug plus placebo electroacupuncture), and Group C (placebo electroacupuncture plus placebo drug). Participants will be exposed to 24 treatments over an 8-week period (3 times per week) and monitored for 12 additional weeks. The primary outcome measure will be changes in brainwave data from before to after the treatment. In addition, the Wisconsin Card Sorting Test and the Pittsburgh Sleep Quality Index will be utilized as secondary outcomes to measure from before to after treatment and during the follow-up. A correlation analysis will be conducted to explore links among modifications in brainwave patterns, Wisconsin Card Sorting Test scores, and Pittsburgh Sleep Quality Index scores. Additionally, any adverse events will be strictly monitored.

DISCUSSION

Electroacupuncture may represent an alternative treatment for chronic insomnia, and this trial is expected to reveal the brain mechanism by which electroacupuncture improves cognitive function and sleep quality in chronic insomnia patients.

TRIAL REGISTRATION

ChiCTR2200060150 (Chinese Clinical Trial Registry, http://www.chictr.org.cn , registered on 20 May 2022).

A systematic comparison of VBM pipelines and their application to age prediction

Voxel-based morphometry (VBM) analysis is commonly used for localized quantification of gray matter volume (GMV). Several alternatives exist to implement a VBM pipeline. However, how these alternatives compare and their utility in applications, such as the estimation of aging effects, remain largely unclear. This leaves researchers wondering which VBM pipeline they should use for their project. In this study, we took a user-centric perspective and systematically compared five VBM pipelines, together with registration to either a general or a study-specific template, utilizing three large datasets (n>500 each). Considering the known effect of aging on GMV, we first compared the pipelines in their ability of individual-level age prediction and found markedly varied results. To examine whether these results arise from systematic differences between the pipelines, we classified them based on their GMVs, resulting in near-perfect accuracy. To gain deeper insights, we examined the impact of different VBM steps using the region-wise similarity between pipelines. The results revealed marked differences, largely driven by segmentation and registration steps. We observed large variability in subject-identification accuracies, highlighting the interpipeline differences in individual-level quantification of GMV. As a biologically meaningful criterion we correlated regional GMV with age. The results were in line with the age-prediction analysis, and two pipelines, CAT and the combination of fMRIPrep for tissue characterization with FSL for registration, reflected age information better.

oFVSD: a Python package of optimized forward variable selection decoder for high-dimensional neuroimaging data

The complexity and high dimensionality of neuroimaging data pose problems for decoding information with machine learning (ML) models because the number of features is often much larger than the number of observations. Feature selection is one of the crucial steps for determining meaningful target features in decoding; however, optimizing the feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML models. Here, we introduce an efficient and high-performance decoding package incorporating a forward variable selection (FVS) algorithm and hyper-parameter optimization that automatically identifies the best feature pairs for both classification and regression models, where a total of 18 ML models are implemented by default. First, the FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross-validation step that identifies the best subset of features based on a predefined criterion for each model. Next, the hyperparameters of each ML model are optimized at each forward iteration. Final outputs highlight an optimized number of selected features (brain regions of interest) for each model with its accuracy. Furthermore, the toolbox can be executed in a parallel environment for efficient computation on a typical personal computer. With the optimized forward variable selection decoder (oFVSD) pipeline, we verified the effectiveness of decoding sex classification and age range regression on 1,113 structural magnetic resonance imaging (MRI) datasets. Compared to ML models without the FVS algorithm and with the Boruta algorithm as a variable selection counterpart, we demonstrate that the oFVSD significantly outperformed across all of the ML models over the counterpart models without FVS (approximately 0.20 increase in correlation coefficient, r, with regression models and 8% increase in classification models on average) and with Boruta variable selection algorithm (approximately 0.07 improvement in regression and 4% in classification models). Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. Altogether, the oFVSD toolbox efficiently and effectively improves the performance of both classification and regression ML models, providing a use case example on MRI datasets. With its flexibility, oFVSD has the potential for many other modalities in neuroimaging. This open-source and freely available Python package makes it a valuable toolbox for research communities seeking improved decoding accuracy.

Comparative neuroimaging of sex differences in human and mouse brain anatomy

In vivo neuroimaging studies have established several reproducible volumetric sex differences in the human brain, but the causes of such differences are hard to parse. While mouse models are useful for understanding the cellular and mechanistic bases of sex-biased brain development in mammals, there have been no attempts to formally compare mouse and human sex differences across the whole brain to ascertain how well they translate. Addressing this question would shed critical light on use of the mouse as a translational model for sex differences in the human brain and provide insights into the degree to which sex differences in brain volume are conserved across mammals. Here, we use cross-species structural magnetic resonance imaging to carry out the first comparative neuroimaging study of sex-biased neuroanatomical organization of the human and mouse brain. In line with previous findings, we observe that in humans, males have significantly larger and more variable total brain volume; these sex differences are not mirrored in mice. After controlling for total brain volume, we observe modest cross-species congruence in the volumetric effect size of sex across 60 homologous brain regions (r=0.30; e.g.: M>F amygdala, hippocampus, bed nucleus of the stria terminalis, and hypothalamus and F>M anterior cingulate, somatosensory, and primary auditory cortices). This cross-species congruence is greater in the cortex (r=0.33) than non-cortex (r=0.16). By incorporating regional measures of gene expression in both species, we reveal that cortical regions with greater cross-species congruence in volumetric sex differences also show greater cross-species congruence in the expression profile of 2835 homologous genes. This phenomenon differentiates primary sensory regions with high congruence of sex effects and gene expression from limbic cortices where congruence in both these features was weaker between species. These findings help identify aspects of sex-biased brain anatomy present in mice that are retained, lost, or inverted in humans. More broadly, our work provides an empirical basis for targeting mechanistic studies of sex-biased brain development in mice to brain regions that best echo sex-biased brain development in humans.

Trust toward humans and trust toward artificial intelligence are not associated: Initial insights from self-report and neurostructural brain imaging

The present study examines whether self-reported trust in humans and self-reported trust in [(different) products with built-in] artificial intelligence (AI) are associated with one another and with brain structure. We sampled 90 healthy participants who provided self-reported trust in humans and AI and underwent brain structural magnetic resonance imaging assessment. We found that trust in humans, as measured by the trust facet of the personality inventory NEO-PI-R, and trust in AI products, as measured by items assessing attitudes toward AI and by a composite score based on items assessing trust toward products with in-built AI, were not significantly correlated. We also used a concomitant dimensional neuroimaging approach employing a data-driven source-based morphometry (SBM) analysis of gray-matter-density to investigate neurostructural associations with each trust domain. We found that trust in humans was negatively (and significantly) correlated with an SBM component encompassing striato-thalamic and prefrontal regions. We did not observe significant brain structural association with trust in AI. The present findings provide evidence that trust in humans and trust in AI seem to be dissociable constructs. While the personal disposition to trust in humans might be "hardwired" to the brain's neurostructural architecture (at least from an individual differences perspective), a corresponding significant link for the disposition to trust AI was not observed. These findings represent an initial step toward elucidating how different forms of trust might be processed on the behavioral and brain level.

Fear of missing out (FOMO) associates with reduced cortical thickness in core regions of the posterior default mode network and higher levels of problematic smartphone and social media use

BACKGROUND AND AIMS

Fear of missing out (FOMO) promotes the desire or urge to stay continuously connected with a social reference group and updated on their activities, which may result in escalating and potentially addictive smartphone and social media use. The present study aimed to determine whether the neurobiological basis of FOMO encompasses core regions of the reward circuitry or social brain, and associations with levels of problematic smartphone or social media use.

METHODS

We capitalized on a dimensional neuroimaging approach to examine cortical thickness and subcortical volume associations in a sample of healthy young individuals (n = 167). Meta-analytic network and behavioral decoding analyses were employed to further characterize the identified regions.

RESULTS

Higher levels of FOMO associated with lower cortical thickness in the right precuneus. In contrast, no associations between FOMO and variations in striatal morphology were observed. Meta-analytic decoding revealed that the identified precuneus region exhibited a strong functional interaction with the default mode network (DMN) engaged in social cognitive and self-referential domains.

DISCUSSION AND CONCLUSIONS

Together the present findings suggest that individual variations in FOMO are associated with the brain structural architecture of the right precuneus, a core hub within a large-scale functional network resembling the DMN and involved in social and self-referential processes. FOMO may promote escalating social media and smartphone use via social and self-referential processes rather than reward-related processes per se.

Neuroimaging the effects of smartphone (over-)use on brain function and structure-a review on the current state of MRI-based findings and a roadmap for future research

The smartphone represents a transformative device that dramatically changed our daily lives, including how we communicate, work, entertain ourselves, and navigate through unknown territory. Given its ubiquitous availability and impact on nearly every aspect of our lives, debates on the potential impact of smartphone (over-)use on the brain and whether smartphone use can be "addictive" have increased over the last years. Several studies have used magnetic resonance imaging to characterize associations between individual differences in excessive smartphone use and variations in brain structure or function. Therefore, it is an opportune time to summarize and critically reflect on the available studies. Following this overview, we present a roadmap for future research to improve our understanding of how excessive smartphone use can affect the brain, mental health, and cognitive and affective functions.

Associations between levels of Internet Gaming Disorder symptoms and striatal morphology-replication and associations with social anxiety

Background

Brain structural alterations of the striatum have been frequently observed in internet gaming disorder (IGD); however, the replicability of the results and the associations with social-affective dysregulations such as social anxiety remain to be determined.

Methods

The present study combined a dimensional neuroimaging approach with both voxel-wise and data-driven multivariate approaches to (i) replicate our previous results on a negative association between IGD symptom load (assessed by the Internet Gaming Disorder Scale-Short Form) and striatal volume, (ii) extend these findings to female individuals, and (iii) employ multivariate and mediation models to determine common brain structural representations of IGD and social anxiety (assessed by the Liebowitz Social Anxiety Scale).

Results

In line with the original study, the voxel-wise analyses revealed a negative association between IGD and volumes of the bilateral caudate. Going beyond the earlier study investigating only male participants, the present study demonstrates that the association in the right caudate was comparable in both the male and the female subsamples. Further examination using the multivariate approach revealed regionally different associations between IGD and social anxiety with striatal density representations in the dorsal striatum (caudate) and ventral striatum (nucleus accumbens). Higher levels of IGD were associated with higher social anxiety and the association was critically mediated by the multivariate neurostructural density variations of the striatum.

Conclusions

Altered striatal volumes may represent a replicable and generalizable marker of IGD symptoms. However, exploratory multivariate analyses revealed more complex and regional specific associations between striatal density and IGD as well as social anxiety symptoms. Variations in both tendencies may share common structural brain representations, which mediate the association between increased IGD and social anxiety.