Whole-body integration of gene expression and single-cell morphology.

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

Genome-Scale Imaging of the 3D Organization and Transcriptional Activity of Chromatin.

The 3D organization of chromatin regulates many genome functions. Our understanding of 3D genome organization requires tools to directly visualize chromatin conformation in its native context. Here we report an imaging technology for visualizing chromatin organization across multiple scales in single cells with high genomic throughput. First we demonstrate multiplexed imaging of hundreds of genomic loci by sequential hybridization, which allows high-resolution conformation tracing of whole chromosomes. Next we report a multiplexed error-robust fluorescence in situ hybridization (MERFISH)-based method for genome-scale chromatin tracing and demonstrate simultaneous imaging of more than 1,000 genomic loci and nascent transcripts of more than 1,000 genes together with landmark nuclear structures. Using this technology, we characterize chromatin domains, compartments, and trans-chromosomal interactions and their relationship to transcription in single cells. We envision broad application of this high-throughput, multi-scale, and multi-modal imaging technology, which provides an integrated view of chromatin organization in its native structural and functional context.

The COMBO window: A chronic cranial implant for multiscale circuit interrogation in mice.

Neuroscientists studying the neural correlates of mouse behavior often lack access to the brain-wide activity patterns elicited during a specific task of interest. Fortunately, large-scale imaging is becoming increasingly accessible thanks to modalities such as Ca2+ imaging and functional ultrasound (fUS). However, these and other techniques often involve challenging cranial window procedures and are difficult to combine with other neuroscience tools. We address this need with an open-source 3D-printable cranial implant-the COMBO (ChrOnic Multimodal imaging and Behavioral Observation) window. The COMBO window enables chronic imaging of large portions of the brain in head-fixed mice while preserving orofacial movements. We validate the COMBO window stability using both brain-wide fUS and multisite two-photon imaging. Moreover, we demonstrate how the COMBO window facilitates the combination of optogenetics, fUS, and electrophysiology in the same animals to study the effects of circuit perturbations at both the brain-wide and single-neuron level. Overall, the COMBO window provides a versatile solution for performing multimodal brain recordings in head-fixed mice.

Multimodal Imaging-Assisted Intravascular Theranostic Photoactivation on Atherosclerotic Plaque.

BACKGROUND: Atherosclerosis is a chronic inflammatory disease causing a fatal plaque rupture, and its key aspect is a failure to resolve inflammation. We hypothesize that macrophage-targeted near-infrared fluorescence emitting photoactivation could simultaneously assess macrophage/lipid-rich plaques in vivo and facilitate inflammation resolution. METHODS: We fabricated a Dectin-1-targeted photoactivatable theranostic agent through the chemical conjugation of the near-infrared fluorescence-emitting photosensitizer chlorin e6 and the Dectin-1 ligand laminarin (laminarin-chlorin e6 [LAM-Ce6]). Intravascular photoactivation by a customized fiber-based diffuser after administration of LAM-Ce6 effectively reduced inflammation in the targeted plaques of atherosclerotic rabbits in vivo as serially assessed by dual-modal optical coherence tomography-near-infrared fluorescence structural-molecular catheter imaging after 4 weeks. RESULTS: The number of apoptotic macrophages peaked at 1 day after laser irradiation and then resolved until 4 weeks. Autophagy was strongly augmented 1 hour after the light therapy, with the formation of autophagolysosomes. LAM-Ce6 photoactivation increased the terminal deoxynucleotidyl transferase dUTP (deoxyuridine triphosphate) nick end labeling/RAM11 (rabbit monocyte/macrophage antibody)- and MerTK (c-Mer tyrosine kinase)-positive cells in the plaques, suggesting enhanced efferocytosis. In line with inflammation resolution, photoactivation reduced the plaque burden through fibrotic replacement via the TGF (transforming growth factor)-ß/CTGF (connective tissue growth factor) pathway. CONCLUSIONS: Optical coherence tomography-near-infrared fluorescence imaging-guided macrophage Dectin-1-targetable photoactivation could induce the transition of macrophage/lipid-rich plaques into collagen-rich lesions through autophagy-mediated inflammation resolution and TGF-ß-dependent fibrotic replacement. This novel strategy offers a new opportunity for the catheter-based theranostic strategy.

VascuViz: a multimodality and multiscale imaging and visualization pipeline for vascular systems biology.

Despite advances in imaging, image-based vascular systems biology has remained challenging because blood vessel data are often available only from a single modality or at a given spatial scale, and cross-modality data are difficult to integrate. Therefore, there is an exigent need for a multimodality pipeline that enables ex vivo vascular imaging with magnetic resonance imaging, computed tomography and optical microscopy of the same sample, while permitting imaging with complementary contrast mechanisms from the whole-organ to endothelial cell spatial scales. To achieve this, we developed 'VascuViz'-an easy-to-use method for simultaneous three-dimensional imaging and visualization of the vascular microenvironment using magnetic resonance imaging, computed tomography and optical microscopy in the same intact, unsectioned tissue. The VascuViz workflow permits multimodal imaging with a single labeling step using commercial reagents and is compatible with diverse tissue types and protocols. VascuViz's interdisciplinary utility in conjunction with new data visualization approaches opens up new vistas in image-based vascular systems biology.

Multimodal imaging analysis in silver fir reveals coordination in cellulose and lignin deposition.

Despite lignin being a key component of wood, the dynamics of tracheid lignification are generally overlooked in xylogenesis studies, which hampers our understanding of environmental drivers and blurs the interpretation of isotopic and anatomical signals stored in tree rings. Here, we analyzed cell wall formation in silver fir (Abies alba Mill.) tracheids to determine if cell wall lignification lags behind secondary wall deposition. For this purpose, we applied a multimodal imaging approach combining transmitted light microscopy (TLM), confocal laser scanning microscopy (CLSM), and confocal Raman microspectroscopy (RMS) on anatomical sections of wood microcores collected in northeast France on 11 dates during the 2010 growing season. Wood autofluorescence after laser excitation at 405 and 488 nm associated with the RMS scattering of lignin and cellulose, respectively, which allowed identification of lignifying cells (cells showing lignified and nonlignified wall fractions at the same time) in CLSM images. The number of lignifying cells in CLSM images mirrored the number of wall-thickening birefringent cells in polarized TLM images, revealing highly synchronized kinetics for wall thickening and lignification (similar timings and durations at the cell level). CLSM images and RMS chemical maps revealed a substantial incorporation of lignin into the wall at early stages of secondary wall deposition. Our results show that most of the cellulose and lignin contained in the cell wall undergo concurrent periods of deposition. This suggests a strong synchronization between cellulose and lignin-related features in conifer tree-ring records, as they originated over highly overlapped time frames.

Multimodality Cardiac Imaging in COVID.

Infection with SARS-CoV-2, the virus that causes COVID, is associated with numerous potential secondary complications. Global efforts have been dedicated to understanding the myriad potential cardiovascular sequelae which may occur during acute infection, convalescence, or recovery. Because patients often present with nonspecific symptoms and laboratory findings, cardiac imaging has emerged as an important tool for the discrimination of pulmonary and cardiovascular complications of this disease. The clinician investigating a potential COVID-related complication must account not only for the relative utility of various cardiac imaging modalities but also for the risk of infectious exposure to staff and other patients. Extraordinary clinical and scholarly efforts have brought the international medical community closer to a consensus on the appropriate indications for diagnostic cardiac imaging during this protracted pandemic. In this review, we summarize the existing literature and reference major societal guidelines to provide an overview of the indications and utility of echocardiography, nuclear imaging, cardiac computed tomography, and cardiac magnetic resonance imaging for the diagnosis of cardiovascular complications of COVID.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Multiple cognition associated multimodal brain networks in major depressive disorder.

Major depressive disorder frequently leads to cognitive impairments, significantly affecting patients' quality of life. However, the neurobiological mechanisms underlying cognitive deficits remain unclear. This study aimed to explore multimodal imaging biomarkers associated with cognitive function in major depressive disorder. Five cognitive scores (sustained attention, visual recognition memory, pattern recognition memory, executive function, and working memory) were used as references to guide the fusion of gray matter volume and amplitude of the low frequency fluctuation. Social function was assessed after 2 yr. Linear regression analysis was performed to identify brain features that were associated with social function of patients with major depressive disorder. Finally, we included 131 major depressive disorder and 145 healthy controls. A multimodal frontal-insula-occipital network associated with sustained attention was found to be associated with social functioning in major depressive disorders. Analysis across different cognitive domains revealed that gray matter volume exhibited greater sensitivity to differences, while amplitude of the low frequency fluctuation consistently decreased in the right temporal-occipital-hippocampus circuit. The consistent functional changes across the 5 cognitive domains were related to symptom severity. Overall, these findings provide insights into biomarkers associated with multiple cognitive domains in major depressive disorder. These results may contribute to the development of effective treatment targeting cognitive deficits and social function.

Do we empathize humanoid robots and humans in the same way? Behavioral and multimodal brain imaging investigations.

Humanoid robots have been designed to look more and more like humans to meet social demands. How do people empathize humanoid robots who look the same as but are essentially different from humans? We addressed this issue by examining subjective feelings, electrophysiological activities, and functional magnetic resonance imaging signals during perception of pain and neutral expressions of faces that were recognized as patients or humanoid robots. We found that healthy adults reported deceased feelings of understanding and sharing of humanoid robots' compared to patients' pain. Moreover, humanoid robot (vs. patient) identities reduced long-latency electrophysiological responses and blood oxygenation level-dependent signals in the left temporoparietal junction in response to pain (vs. neutral) expressions. Furthermore, we showed evidence that humanoid robot identities inhibited a causal input from the right ventral lateral prefrontal cortex to the left temporoparietal junction, contrasting the opposite effect produced by patient identities. These results suggest a neural model of modulations of empathy by humanoid robot identity through interactions between the cognitive and affective empathy networks, which provides a neurocognitive basis for understanding human-robot interactions.

Multimodal optoacoustic imaging: methods and contrast materials.

Optoacoustic (OA) imaging offers powerful capabilities for interrogating biological tissues with rich optical absorption contrast while maintaining high spatial resolution for deep tissue observations. The spectrally distinct absorption of visible and near-infrared photons by endogenous tissue chromophores facilitates extraction of diverse anatomic, functional, molecular, and metabolic information from living tissues across various scales, from organelles and cells to whole organs and organisms. The primarily blood-related contrast and limited penetration depth of OA imaging have fostered the development of multimodal approaches to fully exploit the unique advantages and complementarity of the method. We review the recent hybridization efforts, including multimodal combinations of OA with ultrasound, fluorescence, optical coherence tomography, Raman scattering microscopy and magnetic resonance imaging as well as ionizing methods, such as X-ray computed tomography, single-photon-emission computed tomography and positron emission tomography. Considering that most molecules absorb light across a broad range of the electromagnetic spectrum, the OA interrogations can be extended to a large number of exogenously administered small molecules, particulate agents, and genetically encoded labels. This unique property further makes contrast moieties used in other imaging modalities amenable for OA sensing.

Multimodal Imaging for Validation and Optimization of Ion Channel-Based Chemogenetics in Nonhuman Primates.

Chemogenetic tools provide an opportunity to manipulate neuronal activity and behavior selectively and repeatedly in nonhuman primates (NHPs) with minimal invasiveness. Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) are one example that is based on mutated muscarinic acetylcholine receptors. Another channel-based chemogenetic system available for neuronal modulation in NHPs uses pharmacologically selective actuator modules (PSAMs), which are selectively activated by pharmacologically selective effector molecules (PSEMs). To facilitate the use of the PSAM/PSEM system, the selection and dosage of PSEMs should be validated and optimized for NHPs. To this end, we used a multimodal imaging approach. We virally expressed excitatory PSAM (PSAM4-5HT3) in the striatum and the primary motor cortex (M1) of two male macaque monkeys, and visualized its location through positron emission tomography (PET) with the reporter ligand [18F]ASEM. Chemogenetic excitability of neurons triggered by two PSEMs (uPSEM817 and uPSEM792) was evaluated using [18F]fluorodeoxyglucose-PET imaging, with uPSEM817 being more efficient than uPSEM792. Pharmacological magnetic resonance imaging (phMRI) showed that increased brain activity in the PSAM4-expressing region began ∼13 min after uPSEM817 administration and continued for at least 60 min. Our multimodal imaging data provide valuable information regarding the manipulation of neuronal activity using the PSAM/PSEM system in NHPs, facilitating future applications.SIGNIFICANCE STATEMENT Like other chemogenetic tools, the ion channel-based system called pharmacologically selective actuator module/pharmacologically selective effector molecule (PSAM/PSEM) allows remote manipulation of neuronal activity and behavior in living animals. Nevertheless, its application in nonhuman primates (NHPs) is still limited. Here, we used multitracer positron emission tomography (PET) imaging and pharmacological magnetic resonance imaging (phMRI) to visualize an excitatory chemogenetic ion channel (PSAM4-5HT3) and validate its chemometric function in macaque monkeys. Our results provide the optimal agonist, dose, and timing for chemogenetic neuronal manipulation, facilitating the use of the PSAM/PSEM system and expanding the flexibility and reliability of circuit manipulation in NHPs in a variety of situations.

Microscopy based methods for characterization, drug delivery, and understanding the dynamics of nanoparticles.

Nanomedicine is an emerging field that exploits nanotechnology for the development of novel therapeutic and diagnostic modalities. Researches are been focussed in nanoimaging to develop noninvasive, highly sensitive, and reliable tools for diagnosis and visualization in nanomedical field. The application of nanomedicine in healthcare requires in-depth understanding of their structural, physical and morphological properties, internalization inside living system, biodistribution and localization, stability, mode of action and possible toxic health effects. Microscopic techniques including fluorescence-based confocal laser scanning microscopy, super-resolution fluorescence microscopy and multiphoton microscopy; optical-based Raman microscopy, photoacoustic microscopy and optical coherence tomography; photothermal microscopy; electron microscopy (transmission electron microscope and scanning electron microscope); atomic force microscopy; X-ray microscopy and, correlative multimodal imaging are recognized as an indispensable tool in material research and aided in numerous discoveries. Microscopy holds great promise in detecting the fundamental structures of nanoparticles (NPs) that determines their performance and applications. Moreover, the intricate details that allows assessment of chemical composition, surface topology and interfacial properties, molecular, microstructure, and micromechanical properties are also elucidated. With plethora of applications, microscopy-based techniques have been used to characterize novel NPs alongwith their proficient designing and adoption of safe strategies to be exploited in nanomedicine. Consequently, microscopic techniques have been extensively used in the characterization of fabricated NPs, and their biomedical application in diagnostics and therapeutics. The present review provides an overview of the microscopy-based techniques for in vitro and in vivo application in nanomedical investigation alongwith their challenges and advancement to meet the limitations of conventional methods.

Cardiovascular Imaging in Contemporary Cardio-Oncology: A Scientific Statement From the American Heart Association.

Advances in cancer therapeutics have led to dramatic improvements in survival, now inclusive of nearly 20 million patients and rising. However, cardiovascular toxicities associated with specific cancer therapeutics adversely affect the outcomes of patients with cancer. Advances in cardiovascular imaging have solidified the critical role for robust methods for detecting, monitoring, and prognosticating cardiac risk among patients with cancer. However, decentralized evaluations have led to a lack of consensus on the optimal uses of imaging in contemporary cancer treatment (eg, immunotherapy, targeted, or biological therapy) settings. Similarly, available isolated preclinical and clinical studies have provided incomplete insights into the effectiveness of multiple modalities for cardiovascular imaging in cancer care. The aims of this scientific statement are to define the current state of evidence for cardiovascular imaging in the cancer treatment and survivorship settings and to propose novel methodological approaches to inform the optimal application of cardiovascular imaging in future clinical trials and registries. We also propose an evidence-based integrated approach to the use of cardiovascular imaging in routine clinical settings. This scientific statement summarizes and clarifies available evidence while providing guidance on the optimal uses of multimodality cardiovascular imaging in the era of emerging anticancer therapies.

Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links.

In recent years, deep learning approaches have gained significant attention in predicting brain disorders using neuroimaging data. However, conventional methods often rely on single-modality data and supervised models, which provide only a limited perspective of the intricacies of the highly complex brain. Moreover, the scarcity of accurate diagnostic labels in clinical settings hinders the applicability of the supervised models. To address these limitations, we propose a novel self-supervised framework for extracting multiple representations from multimodal neuroimaging data to enhance group inferences and enable analysis without resorting to labeled data during pre-training. Our approach leverages Deep InfoMax (DIM), a self-supervised methodology renowned for its efficacy in learning representations by estimating mutual information without the need for explicit labels. While DIM has shown promise in predicting brain disorders from single-modality MRI data, its potential for multimodal data remains untapped. This work extends DIM to multimodal neuroimaging data, allowing us to identify disorder-relevant brain regions and explore multimodal links. We present compelling evidence of the efficacy of our multimodal DIM analysis in uncovering disorder-relevant brain regions, including the hippocampus, caudate, insula, - and multimodal links with the thalamus, precuneus, and subthalamus hypothalamus. Our self-supervised representations demonstrate promising capabilities in predicting the presence of brain disorders across a spectrum of Alzheimer's phenotypes. Comparative evaluations against state-of-the-art unsupervised methods based on autoencoders, canonical correlation analysis, and supervised models highlight the superiority of our proposed method in achieving improved classification performance, capturing joint information, and interpretability capabilities. The computational efficiency of the decoder-free strategy enhances its practical utility, as it saves compute resources without compromising performance. This work offers a significant step forward in addressing the challenge of understanding multimodal links in complex brain disorders, with potential applications in neuroimaging research and clinical diagnosis.

GAN-MAT: Generative adversarial network-based microstructural profile covariance analysis toolbox.

Multimodal magnetic resonance imaging (MRI) provides complementary information for investigating brain structure and function; for example, an in vivo microstructure-sensitive proxy can be estimated using the ratio between T1- and T2-weighted structural MRI. However, acquiring multiple imaging modalities is challenging in patients with inattentive disorders. In this study, we proposed a comprehensive framework to provide multiple imaging features related to the brain microstructure using only T1-weighted MRI. Our toolbox consists of (i) synthesizing T2-weighted MRI from T1-weighted MRI using a conditional generative adversarial network; (ii) estimating microstructural features, including intracortical covariance and moment features of cortical layer-wise microstructural profiles; and (iii) generating a microstructural gradient, which is a low-dimensional representation of the intracortical microstructure profile. We trained and tested our toolbox using T1- and T2-weighted MRI scans of 1,104 healthy young adults obtained from the Human Connectome Project database. We found that the synthesized T2-weighted MRI was very similar to the actual image and that the synthesized data successfully reproduced the microstructural features. The toolbox was validated using an independent dataset containing healthy controls and patients with episodic migraine as well as the atypical developmental condition of autism spectrum disorder. Our toolbox may provide a new paradigm for analyzing multimodal structural MRI in the neuroscience community and is openly accessible at https://github.com/CAMIN-neuro/GAN-MAT.

Synaptic signaling modeled by functional connectivity predicts metabolic demands of the human brain.

PURPOSE: The human brain is characterized by interacting large-scale functional networks fueled by glucose metabolism. Since former studies could not sufficiently clarify how these functional connections shape glucose metabolism, we aimed to provide a neurophysiologically-based approach. METHODS: 51 healthy volunteers underwent simultaneous PET/MRI to obtain BOLD functional connectivity and [18F]FDG glucose metabolism. These multimodal imaging proxies of fMRI and PET were combined in a whole-brain extension of metabolic connectivity mapping. Specifically, functional connectivity of all brain regions were used as input to explain glucose metabolism of a given target region. This enabled the modeling of postsynaptic energy demands by incoming signals from distinct brain regions. RESULTS: Functional connectivity input explained a substantial part of metabolic demands but with pronounced regional variations (34 - 76%). During cognitive task performance this multimodal association revealed a shift to higher network integration compared to resting state. In healthy aging, a dedifferentiation (decreased segregated/modular structure of the brain) of brain networks during rest was observed. Furthermore, by including data from mRNA maps, [11C]UCB-J synaptic density and aerobic glycolysis (oxygen-to-glucose index from PET data), we show that whole-brain functional input reflects non-oxidative, on-demand metabolism of synaptic signaling. The metabolically-derived directionality of functional inputs further marked them as top-down predictions. In addition, the approach uncovered formerly hidden networks with superior efficiency through metabolically informed network partitioning. CONCLUSIONS: Applying multimodal imaging, we decipher a crucial part of the metabolic and neurophysiological basis of functional connections in the brain as interregional on-demand synaptic signaling fueled by anaerobic metabolism. The observed task- and age-related effects indicate promising future applications to characterize human brain function and clinical alterations.

Concurrent behavioral modeling and multimodal neuroimaging reveals how feedback affects the performance of decision making in internet gaming disorder.

Internet gaming disorder (IGD) prompts inquiry into how feedback from prior gaming rounds influences subsequent risk-taking behavior and potential neural mechanisms. Forty-two participants, including 15 with IGD and 27 health controls (HCs), underwent a sequential risk-taking task. Hierarchy Bayesian modeling was adopted to measure risky propensity, behavioral consistence, and affection by emotion ratings from last trial. Concurrent electroencephalogram and functional near-infrared spectroscopy (EEG-fNIRS) recordings were performed to demonstrate when, where and how the previous-round feedback affects the decision making to the next round. We discovered that the IGD illustrated heightened risk-taking propensity as compared to the HCs, indicating by the computational modeling (p = 0.028). EEG results also showed significant time window differences in univariate and multivariate pattern analysis between the IGD and HCs after the loss of the game. Further, reduced brain activation in the prefrontal cortex during the task was detected in IGD as compared to that of the control group. The findings underscore the importance of understanding the aberrant decision-making processes in IGD and suggest potential implications for future interventions and treatments aimed at addressing this behavioral addiction.

Multimodal neuroimaging fusion unravel structural-functional-neurotransmitter change in Parkinson's disease with impulse control disorders.

BACKGROUND: Impulse control disorders (ICD) in Parkinson's disease (PD) is highly multifactorial in etiology and has intricate neural mechanisms. Our multimodal neuroimaging study aimed to investigate the specific patterns of structure-function-neurotransmitter interactions underlying ICD. METHODS: Thirty PD patients with ICD (PD-ICD), 30 without ICD (PD-NICD) and 32 healthy controls (HCs) were recruited. Gyrification and perivascular spaces (PVS) were computed to capture the alternations of cortical surface morphology and glymphatic function. Seed-based functional connectivity (FC) were performed to identify the corresponding functional changes. Further, JuSpace toolbox were employed for cross-modal correlations to evaluate whether the spatial patterns of functional alterations in ICD patients were associated with specific neurotransmitter system. RESULTS: Compared to PD-NICD, PD-ICD patients showed hypogyrification and enlarged PVS volume fraction in the left orbitofrontal gyrus (OFG), as well as decreased FC between interhemispheric OFG. The interhemispheric OFG connectivity reduction was associated with spatial distribution of µ-opioid pathway (r = -0.186, p = 0.029, false discovery rate corrected). ICD severity was positively associated with the PVS volume fraction of left OFG (r = 0.422, p = 0.032). Furthermore, gyrification index (LGI) and percent PVS (pPVS) in OFG and their combined indicator showed good performance in differentiating PD-ICD from PD-NICD. CONCLUSIONS: Our findings indicated that the co-altered structure-function-neurotransmitter interactions of OFG might be involved in the pathogenesis of ICD.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

BACKGROUND: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits. METHODS: We included 59 patients with PD and 41 age- and sex-matched healthy controls (HCs). The voxel-wise mean amplitude of the low-frequency fluctuation (mALFF) was calculated and compared between the two groups. The JuSpace toolbox was used to test whether spatial patterns of mALFF alterations in patients with PD were associated with specific neurotransmitter receptor/transporter densities. RESULTS: Compared to HCs, patients with PD showed reduced mALFF in the sensorimotor- and visual-related regions. In addition, mALFF alteration patterns were significantly associated with the spatial distribution of the serotonergic, dopaminergic, noradrenergic, glutamatergic, cannabinoid, and acetylcholinergic neurotransmitter systems (p < 0.05, false discovery rate-corrected). CONCLUSIONS: Our results revealed abnormal brain activity patterns and specific neurotransmitter deficits in patients with PD, which may provide new insights into the mechanisms and potential targets for pharmacotherapy.