Neuroimaging Analysis Research Articles

The cortical regions and white matter tracts underlying auditory comprehension in patients with primary brain tumor.

The comprehension of spoken language is one of the most essential language functions in humans. However, the neurological underpinnings of auditory comprehension remain under debate. Here we used multi-modal neuroimaging analyses on a group of patients with low-grade gliomas to localize cortical regions and white matter tracts responsible for auditory language comprehension. Region-of-interests and voxel-level whole-brain analyses showed that cortical areas in the posterior temporal lobe are crucial for language comprehension. The fiber integrity assessed with diffusion tensor imaging of the arcuate fasciculus and the inferior longitudinal fasciculus was strongly correlated with both auditory comprehension and the grey matter volume of the inferior temporal and middle temporal gyri. Together, our findings provide direct evidence for an integrated network of auditory comprehension whereby the superior temporal gyrus and sulcus, the posterior parts of the middle and inferior temporal gyri serve as auditory comprehension cortex, and the arcuate fasciculus and the inferior longitudinal fasciculus subserve as crucial structural connectivity. These findings provide critical evidence on the neural underpinnings of language comprehension.

Association Between Insulin-Like Growth Factor-1 and Social Cognition in Huntington's Disease.

Insulin-like growth factor 1 (IGF-1) seems to be involved in the neural circuits associated with social cognition and brain structure. To investigate the association of IGF-1 levels with social cognition and brain structure in Huntington's disease (HD). We evaluated social cognition using the Ekman test in 22 HD patients and 19 matched controls. Brain structure was assessed using standard volume-based voxel-based morphometry and surface-based cortical thickness pipeline. We analyzed the association of IGF-1 levels with social cognition and brain structure using adjusted regression analysis. Social cognition was worse in HD patients (P < 0.001), on antidopaminergic drugs (P=0.02), and with lower IGF-1 levels (P=0.04). In neuroimaging analyses, lower IGF-1 levels were associated with social cognition impairment and atrophy mainly in frontotemporal regions (P < 0.05 corrected). In HD, abnormal IGF-1 function seems to be associated with brain atrophy leading to clinical deficits in social cognition.

Post-stroke hand synergy formation variant. Clinical case

Pathological synergies are a frequent consequence of cerebrovascular accidents and hinders further recovery. The existing concept of the formation of pathological synergies considers them as a compensatory strategy in response to damage to the pyramidal tract, which, due to paresis and increased muscle tone, has acquired a pathological character. Recent studies in primates have shown that the contralateral hemisphere, in particular the reticulospinal and rubrospinal tracts, may be involved in motor control of the hand. The current hypothesis presents the corticoreticulospinal and corticorubrospinal tracts as a back-up system for neuronal reorganization due to injury.&#x0D; This clinical case describes the role of the white matter of the contralateral hemisphere in the mechanism of formation of pathological flexion synergy, based on data from the analysis of movements and neuroimaging.

Subjective cancer-related cognitive impairments and salience network connectivity in breast cancer survivors.

Little is known about the neural basis of subjective cancer-related cognitive changes. The purpose of this study was to explore salience network connectivity in relation to subjective executive and memory dysfunction in breast cancer survivors compared to controls. A retrospective cross-sectional analysis of neuroimaging, subjective cognitive, clinical, and demographic data in chemotherapy-treated primary breast cancer survivors compared to frequency matched controls was used. Functional connectivity within salience network hubs (anterior cingulate, bilateral insula) was determined using resting state functional MRI. Mann-Whitney U tests were used to evaluate group differences and Spearman's rho correlations were examined among the behavioral measures and salience network connectivity. We included 65 breast cancer survivors and 71 controls. Survivors demonstrated greater subjective executive dysfunction and memory complaints (p < .001) and lower salience network connectivity (p < .05) than controls. Executive functioning correlated with bilateral insula and left anterior cingulate connectivity (rho > - 0.29, p < .05). Distress did not correlate with salience network connectivity. These findings suggest that salience network connectivity may represent a biomarker of subjective cancer-related cognitive changes. Subjective cancer-related cognitive changes are common following treatment and associated with objective changes in brain connectivity.

Plasma Amyloid‐β 40 as a Potential Biomarker in Cerebral Amyloid Angiopathy

AbstractBackgroundWe aimed to analyze plasma amyloid‐β (Aβ) using a highly sensitive nano‐biosensing platform to demonstrate the possibility of precise cerebral amyloid angiopathy (CAA) diagnosis in participants classified according to amyloid PET positivity and the neuroimaging criteria for CAA.MethodA total of 25 normal controls (NC) and 19 patients with Alzheimer’s disease (AD), which were further classified into the CAA− and CAA+ groups according to the modified Boston criteria were recruited in this study. All participants underwent plasma Aβ analysis using a highly sensitive nano‐biosensing platform, amyloid PET scanning, and detailed neuropsychological testing.ResultThe average signal levels of plasma Aβ42/40 differed significantly between the NC and AD groups, and the CAA+ group exhibited significantly higher plasma Aβ40 signal levels than the CAA− group in both NC and AD groups. The concordance between the plasma Aβ40 signal level and the neuroimaging criteria for CAA was nearly perfect. Higher plasma Aβ40 signal levels were significantly associated with the presence of CAA based on the regression analyses, and the neuroimaging pattern analysis partly supported this result.ConclusionOur findings suggest that measuring plasma Aβ40 using a highly sensitive nano‐biosensing platform could be a useful non‐invasive diagnostic method for detecting CAA.

Identifying the neural marker of chronic sciatica using multimodal neuroimaging and machine learning analyses.

Sciatica is a pain disorder often caused by the herniated disk compressing the lumbosacral nerve roots. Neuroimaging studies have identified functional abnormalities in patients with chronic sciatica (CS). However, few studies have investigated the neural marker of CS using brain structure and the classification value of multidimensional neuroimaging features in CS patients is unclear. Here, structural and resting-state functional magnetic resonance imaging (fMRI) was acquired for 34 CS patients and 36 matched healthy controls (HCs). We analyzed cortical surface area, cortical thickness, amplitude of low-frequency fluctuation (ALFF), regional homogeneity (REHO), between-regions functional connectivity (FC), and assessed the correlation between neuroimaging measures and clinical scores. Finally, the multimodal neuroimaging features were used to differentiate the CS patients and HC individuals by support vector machine (SVM) algorithm. Compared to HC, CS patients had a larger cortical surface area in the right banks of the superior temporal sulcus and rostral anterior cingulate; higher ALFF value in the left inferior frontal gyrus; enhanced FCs between somatomotor and ventral attention network. Three FCs values were associated with clinical pain scores. Furthermore, the three multimodal neuroimaging features with significant differences between groups and the SVM algorithm could classify CS patients and HC with an accuracy of 90.00%. Together, our findings revealed extensive reorganization of local functional properties, surface area, and network metrics in CS patients. The success of patient identification highlights the potential of using artificial intelligence and multimodal neuroimaging markers in chronic pain research.

Abnormal cortical thickness and structural covariance networks in systemic lupus erythematosus patients without major neuropsychiatric manifestations

BackgroundNon-neuropsychiatric systemic lupus erythematosus (non-NPSLE) has been confirmed to have subtle changes in brain structure before the appearance of obvious neuropsychiatric symptoms. Previous literature mainly focuses on brain structure loss in non-NPSLE; however, the results are heterogeneous, and the impact of structural changes on the topological structure of patients’ brain networks remains to be determined. In this study, we combined neuroimaging and network analysis methods to evaluate the changes in cortical thickness and its structural covariance networks (SCNs) in patients with non-NPSLE.MethodsWe compare the cortical thickness of non-NPSLE patients (N=108) and healthy controls (HCs, N=88) using both surface-based morphometry (SBM) and regions of interest (ROI) methods, respectively. After that, we analyzed the correlation between the abnormal cortical thickness results found in the ROI method and a series of clinical features. Finally, we constructed the SCNs of two groups using the regional cortical thickness and analyzed the abnormal SCNs of non-NPSLE.ResultsBy SBM method, we found that cortical thickness of 34 clusters in the non-NPSLE group was thinner than that in the HC group. ROI method based on Destrieux atlas showed that cortical thickness of 57 regions in the non-NPSLE group was thinner than that in the HC group and related to the course of disease, autoantibodies, the cumulative amount of immunosuppressive agents, and cognitive psychological scale. In the SCN analysis, the cortical thickness SCNs of the non-NPSLE group did not follow the small-world attribute at a few densities, and the global clustering coefficient appeared to increase. The area under the curve analysis showed that there were significant differences between the two groups in clustering coefficient, degree, betweenness, and local efficiency. There are a total of seven hubs for non-NPSLE, and five hubs in HCs, the two groups do not share a common hub distribution.ConclusionExtensive and obvious reduction in cortical thickness and abnormal topological organization of SCNs are observed in non-NPSLE patients. The observed abnormalities may not only be the realization of brain damage caused by the disease, but also the contribution of the compensatory changes within the nervous system.

The music of the hemispheres: Cortical eigenmodes as a physical basis for large-scale brain activity and connectivity patterns.

Neuroscience has had access to high-resolution recordings of large-scale cortical activity and structure for decades, but still lacks a generally adopted basis to analyze and interrelate results from different individuals and experiments. Here it is argued that the natural oscillatory modes of the cortex-cortical eigenmodes-provide a physically preferred framework for systematic comparisons across experimental conditions and imaging modalities. In this framework, eigenmodes are analogous to notes of a musical instrument, while commonly used statistical patterns parallel frequently played chords. This intuitive perspective avoids problems that often arise in neuroimaging analyses, and connects to underlying mechanisms of brain activity. We envisage this approach will lead to novel insights into whole-brain function, both in existing and prospective datasets, and facilitate a unification of empirical findings across presently disparate analysis paradigms and measurement modalities.

Beyond the brain localization of complex traits: Distributed white matter markers of personality.

Extensive work in personality neuroscience has shown mixed results in the ability to localize reliable relationships between personality traits and neuroimaging measures. However, recent work in translational neuroimaging has recognized that multifaceted psychological dispositions are not represented in discrete, highly localized brain areas. As such, standard univariate neuroimaging analyses may not be well-suited for capturing broad personality traits supported by distributed networks. The present study uses an out-of-sample predictive modeling approach to identify multivariate signatures of Big Five personality traits within the structural integrity of white matter pathways using diffusion magnetic resonance imaging. In Study 1 (N=491), we trained a ridge regression model to predict each of the Big Five traits and tested these models in an independent hold-out subsample. We found that models for both Neuroticism and Openness were significantly related to predictive accuracy in the hold-out sample. Study 2 (N=108) applied Study 1's predictive models to an independent set of data collected on a different scanner and using a different Big Five scale. Here, we found that the model for Neuroticism remained a significant predictor of individual difference. Our findings provide evidence that this white matter signature of Neuroticism generalizes across differences in measurement and samples.

Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process

Blind source separation (BSS) aims to separate latent source signals from their mixtures. For spatially dependent signals in high-dimensional and large-scale data, such as neuroimaging, most existing BSS methods do not take into account the spatial dependence and the sparsity of the latent source signals. To address these major limitations, we propose a Bayesian spatial blind source separation (BSP-BSS) approach for neuroimaging data analysis. We assume the expectation of the observed images as a linear mixture of multiple sparse and piece-wise smooth latent source signals, for which we construct a new class of Bayesian nonparametric prior models by thresholding Gaussian processes. We assign the vMF priors to mixing coefficients in the model. Under some regularity conditions, we show that the proposed method has several desirable theoretical properties including the large support for the priors, the consistency of joint posterior distribution of the latent source intensity functions and the mixing coefficients, and the selection consistency on the number of latent sources. We use extensive simulation studies and an analysis of the resting-state fMRI data in the Autism Brain Imaging Data Exchange (ABIDE) study to demonstrate that BSP-BSS outperforms the existing method for separating latent brain networks and detecting activated brain activation in the latent sources. Supplementary materials for this article are available online.

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

Machine learning based risk prediction for Parkinson's disease with nationwide health screening data

Although many studies have been conducted on machine learning (ML) models for Parkinson’s disease (PD) prediction using neuroimaging and movement analyses, studies with large population-based datasets are limited. We aimed to propose PD prediction models using ML algorithms based on the National Health Insurance Service-Health Screening datasets. We selected individuals who participated in national health-screening programs > 5 times between 2002 and 2015. PD was defined based on the ICD-code (G20), and a matched cohort of individuals without PD was selected using a 1:1 random sampling method. Various ML algorithms were applied for PD prediction, and the performance of the prediction models was compared. Neural networks, gradient boosting machines, and random forest algorithms exhibited the best average prediction accuracy (average area under the receiver operating characteristic curve (AUC): 0.779, 0.766, and 0.731, respectively) among the algorithms validated in this study. The overall model performance metrics were higher in men than in women (AUC: 0.742 and 0.729, respectively). The most important factor for predicting PD occurrence was body mass index, followed by total cholesterol, glucose, hemoglobin, and blood pressure levels. Smoking and alcohol consumption (in men) and socioeconomic status, physical activity, and diabetes mellitus (in women) were highly correlated with the occurrence of PD. The proposed health-screening dataset-based PD prediction model using ML algorithms is readily applicable, produces validated results, and could be a useful option for PD prediction models.

NEMAR: an open access data, tools and compute resource operating on neuroelectromagnetic data.

To preserve scientific data created by publicly and/or philanthropically funded research projects and to make it ready for exploitation using recent and ongoing advances in advanced and large-scale computational modeling methods, publicly available data must use in common, now-evolving standards for formatting, identifying and annotating should share data. The OpenNeuro.org archive, built first as a repository for magnetic resonance imaging data based on the Brain Imaging Data Structure formatting standards, aims to house and share all types of human neuroimaging data. Here, we present NEMAR.org, a web gateway to OpenNeuro data for human neuroelectromagnetic data. NEMAR allows users to search through, visually explore and assess the quality of shared electroencephalography (EEG), magnetoencephalography and intracranial EEG data and then to directly process selected data using high-performance computing resources of the San Diego Supercomputer Center via the Neuroscience Gateway (nsgportal.org, NSG), a freely available web portal to high-performance computing serving a variety of neuroscientific analysis environments and tools. Combined, OpenNeuro, NEMAR and NSG form an efficient, integrated data, tools and compute resource for human neuroimaging data analysis and meta-analysis. Database URL: https://nemar.org.

Cognitive Resilience in Brain Health and Dementia Research.

With the expected rise in Alzheimer's disease and related dementias (ADRD) in the coming decades due to the aging population and a lack of effective disease-modifying treatments, there is a need for preventive strategies that may tap into resilience parameters. A wide array of resilience strategies has been proposed including genetics, socioeconomic status, lifestyle modifications, behavioral changes, and management of comorbid disease. These different strategies can be broadly classified as distinguishing between modifiable and non-modifiable risk factors, some of which can be quantified so that their clinical intervention can be effectively accomplished. A clear shift in research focus from dementia risk to addressing disease resistance and resilience is emerging that has provided new potential therapeutic targets. Here we review and summarize the latest investigations of resilience mechanisms and methods of quantifying resilience for clinical research. These approaches include identifying genetic variants that may help identify novel pathways (e.g., lipid metabolism, cellular trafficking, synaptic function, inflammation) for therapeutic treatments and biomarkers for use in a precision medicine-like regimen. In addition, innovative structural and molecular neuroimaging analyses may assist in detecting and quantifying pathological changes well before the onset of clinical symptoms setting up the possibility of primary and secondary prevention trials. Lastly, we summarize recent studies demonstrating the study of resilience in caregivers of persons living with dementia may have direct and indirect impact on the quality of care and patient outcomes.

RF01 | PMON43 Melanocortin-4 Receptor Agonism Enhances Sexual Brain Processing in Women with Hypoactive Sexual Desire Disorder

Abstract Introduction Hypoactive sexual desire disorder (HSDD) is characterized by a persistent deficiency of sexual fantasies and desire for sexual activity, causing marked distress or interpersonal difficulty. It is the most prevalent female sexual health problem worldwide, affecting approximately 1 in 10 women, but has limited treatment options despite its substantial health, social, and economic burden. Melanocortin-4 receptor (MC4R) agonists have emerged as a promising therapy for women with HSDD, although, to date, their mechanism of action is unknown. This study aims to use functional MRI to delineate the reproductive neuroendocrine pathways involved and elucidate how MC4R agonists treat HSDD in women. Methods We conducted a randomized, double-blinded, placebo-controlled, crossover clinical study, using psychometric, functional neuroimaging and hormonal analyses, to assess the effects of MC4R agonism on sexual brain processing in 31 premenopausal women with HSDD. Participants attended on two occasions, receiving either a subcutaneous injection of an MC4R agonist (Bremelanotide 1.75 mg), or placebo, at each study visit, thereby acting as their own controls. Results 31 randomized women were included (mean age 32 years, mean duration of HSDD 41 months). MC4R agonism significantly increased self-reported sexual desire for up to 24-hours post administration, compared to placebo (P=0.007). During functional neuroimaging, MC4R agonism enhanced cerebellar and supplementary motor area activity, as well as deactivating the secondary somatosensory cortex, specifically in response to visual erotic videos, compared to placebo (Z=2.3, P&amp;lt;0.05). In addition, MC4R agonism enhanced functional connectivity between the amygdala-insula and amygdala-thalamus during prolonged visual erotic stimuli, compared to placebo (P=0.025). MC4R agonism resulted in a mean increase in LH of 1.1 iU/L (F [1,58] = 13.38, P=0.0005), and FSH of 0.35 iU/L (F [1,60] = 10.97, P=0.0016) across the 300-minute duration of the study, with no effect observed on circulating estradiol or progesterone levels. Interpretation of results and conclusions:We demonstrate that MC4R agonism deactivates the secondary somatosensory cortex which can reduce the detrimental self-monitoring process often observed in HSDD, thereby increasing sexual desire. Furthermore, cerebellar and supplementary motor area activation by MC4R agonism are associated with increased sexual arousal and sexual motor imagery respectively. Finally, MC4R agonism enhanced functional connectivity between key limbic structures which can be disrupted in HSDD. In summary, these data identify the previously undescribed neural substrates and connections through which MC4R agonism modulates sexual brain processing to increase sexual desire. These findings provide mechanistic insight for the action of MC4R agonism in sexual behaviour and are relevant to ongoing therapeutic development for HSDD and for MC4R agonist development more widely. Trial registration: ClinicalTrials.gov identifier NCT04179734.Funding: Investigator-sponsored study funded by AMAG Pharmaceuticals Inc, MRC and NIHR. Presentation: Saturday, June 11, 2022 1:00 p.m. - 1:05 p.m., Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.

Site effects how-to and when: An overview of retrospective techniques to accommodate site effects in multi-site neuroimaging analyses.

Site differences, or systematic differences in feature distributions across multiple data-acquisition sites, are a known source of heterogeneity that may adversely affect large-scale meta- and mega-analyses of independently collected neuroimaging data. They influence nearly all multi-site imaging modalities and biomarkers, and methods to compensate for them can improve reliability and generalizability in the analysis of genetics, omics, and clinical data. The origins of statistical site effects are complex and involve both technical differences (scanner vendor, head coil, acquisition parameters, imaging processing) and differences in sample characteristics (inclusion/exclusion criteria, sample size, ancestry) between sites. In an age of expanding international consortium research, there is a growing need to disentangle technical site effects from sample characteristics of interest. Numerous statistical and machine learning methods have been developed to control for, model, or attenuate site effects - yet to date, no comprehensive review has discussed the benefits and drawbacks of each for different use cases. Here, we provide an overview of the different existing statistical and machine learning methods developed to remove unwanted site effects from independently collected neuroimaging samples. We focus on linear mixed effect models, the ComBat technique and its variants, adjustments based on image quality metrics, normative modeling, and deep learning approaches such as generative adversarial networks. For each method, we outline the statistical foundation and summarize strengths and weaknesses, including their assumptions and conditions of use. We provide information on software availability and comment on the ease of use and the applicability of these methods to different types of data. We discuss validation and comparative reports, mention caveats and provide guidance on when to use each method, depending on context and specific research questions.

Brain network architecture constrains age-related cortical thinning

Age-related cortical atrophy, approximated by cortical thickness measurements from magnetic resonance imaging, follows a characteristic pattern over the lifespan. Although its determinants remain unknown, mounting evidence demonstrates correspondence between the connectivity profiles of structural and functional brain networks and cortical atrophy in health and neurological disease. Here, we performed a cross-sectional multimodal neuroimaging analysis of 2633 individuals from a large population-based cohort to characterize the association between age-related differences in cortical thickness and functional as well as structural brain network topology. We identified a widespread pattern of age-related cortical thickness differences including “hotspots” of pronounced age effects in sensorimotor areas. Regional age-related differences were strongly correlated within the structurally defined node neighborhood. The overall pattern of thickness differences was found to be anchored in the functional network hierarchy as encoded by macroscale functional connectivity gradients. Lastly, the identified difference pattern covaried significantly with cognitive and motor performance. Our findings indicate that connectivity profiles of functional and structural brain networks act as organizing principles behind age-related cortical thinning as an imaging surrogate of cortical atrophy.

Deep learning in neuroimaging data analysis: Applications, challenges, and solutions.

Methods for the analysis of neuroimaging data have advanced significantly since the beginning of neuroscience as a scientific discipline. Today, sophisticated statistical procedures allow us to examine complex multivariate patterns, however most of them are still constrained by assuming inherent linearity of neural processes. Here, we discuss a group of machine learning methods, called deep learning, which have drawn much attention in and outside the field of neuroscience in recent years and hold the potential to surpass the mentioned limitations. Firstly, we describe and explain the essential concepts in deep learning: the structure and the computational operations that allow deep models to learn. After that, we move to the most common applications of deep learning in neuroimaging data analysis: prediction of outcome, interpretation of internal representations, generation of synthetic data and segmentation. In the next section we present issues that deep learning poses, which concerns multidimensionality and multimodality of data, overfitting and computational cost, and propose possible solutions. Lastly, we discuss the current reach of DL usage in all the common applications in neuroimaging data analysis, where we consider the promise of multimodality, capability of processing raw data, and advanced visualization strategies. We identify research gaps, such as focusing on a limited number of criterion variables and the lack of a well-defined strategy for choosing architecture and hyperparameters. Furthermore, we talk about the possibility of conducting research with constructs that have been ignored so far or/and moving toward frameworks, such as RDoC, the potential of transfer learning and generation of synthetic data.

Effects of Kisspeptin Administration in Women With Hypoactive Sexual Desire Disorder

Despite being the most common female sexual health complaint worldwide, current treatment options for hypoactive sexual desire disorder (HSDD) are limited in their safety and effectiveness. The hormone kisspeptin is a key endogenous activator of the reproductive hormonal axis with additional emerging roles in sexual and emotional behavior; however, its effects in women with HSDD are unknown. To test the hypothesis that kisspeptin enhances sexual and attraction brain processing in women with HSDD. This randomized clinical trial was double-masked and placebo controlled with a 2-way crossover. The trial was conducted in a university research setting in the UK from October 2020 to April 2021. Eligible participants were premenopausal women with HSDD. Functional neuroimaging, psychometric, and hormonal analyses were employed to investigate the effects of kisspeptin administration on brain processing, in response to erotic stimuli (erotic videos) and facial attraction (face images of varying attractiveness). Data were analyzed from May to December 2021. A 75-minute intravenous infusion of kisspeptin-54 (1 nmol/kg/h) vs equivalent-rate placebo infusion. Blood oxygen level-dependent responses across the whole brain and regions of interest during kisspeptin vs placebo administration in response to erotic and facial attraction stimuli. Of the 40 participants who were randomized, 32 women completed both kisspeptin and placebo visits, with a mean (SE) age of 29.2 (1.2) years. Kisspeptin administration resulted in modulations in sexual and facial attraction brain processing (deactivation of the left inferior frontal gyrus: Z max, 3.76; P = .01; activation of the right postcentral and supramarginal gyrus: Z max, 3.73; P < .001; deactivation of the right temporoparietal junction: Z max 4.08; P = .02). Furthermore, positive correlations were observed between kisspeptin-enhanced hippocampal activity in response to erotic videos, and baseline distress relating to sexual function (r = 0.469; P = .007). Kisspeptin's enhancement of posterior cingulate cortex activity in response to attractive male faces also correlated with reduced sexual aversion, providing additional functional significance (r = 0.476, P = .005). Kisspeptin was well-tolerated with no reported adverse effects. These findings lay the foundations for clinical applications for kisspeptin in women with HSDD. ISRCTN trial registry identifier: ISRCTN17271094.

Building a tissue-unbiased brain template of fiber orientation distribution and tractography with multimodal registration.

Brain templates provide an essential standard space for statistical analysis of brain structure and function. Despite recent advances, diffusion MRI still lacks a template of fiber orientation distribution (FOD) and tractography that is unbiased for both white and gray matter. Therefore, we aim to build up a set of such templates for better white-matter analysis and joint structural and functional analysis. We have developed a multimodal registration method to leverage the complementary information captured by T1 -weighted, T2 -weighted, and diffusion MRI, so that a coherent transformation is generated to register FODs into a common space and average them into a template. Consequently, the anatomically constrained fiber-tracking method was applied to the FOD template to generate a tractography template. Fiber-centered functional connectivity analysis was then performed as an example of the benefits of such an unbiased template. Our FOD template preserves fine structural details in white matter and also, importantly, clear folding patterns in the cortex and good contrast in the subcortex. Quantitatively, our templates show better individual-template agreement at the whole-brain scale and segmentation scale. The tractography template aligns well with the gray matter, which led to fiber-centered functional connectivity showing high cross-group consistency. We have proposed a novel methodology for building a tissue-unbiased FOD and anatomically constrained tractography template based on multimodal registration. Our templates provide a standard space and statistical platform for not only white-matter analysis but also joint structural and functional analysis, therefore filling an important gap in multimodal neuroimage analysis.