Identifying brain networks in synaptic density PET (11C-UCB-J) with independent component analysis

An In Vivo High-Resolution Human Brain Atlas of Synaptic Density

Synapses are fundamental to the function of the central nervous system and are implicated in a number of brain disorders. Despite their pivotal role, a comprehensive imaging resource detailing the distribution of synapses in the human brain has been lacking until now. Here, we employ high-resolution PET neuroimaging in healthy humans (17F/16M) to create a 3D atlas of the synaptic marker Synaptic Vesicle glycoprotein 2A (SV2A). Calibration to absolute density values (pmol/ml) was achieved by leveraging postmortem human brain autoradiography data. The atlas unveils distinctive cortical and subcortical gradients of synapse density that reflect functional topography and hierarchical order from core sensory to higher-order integrative areas-a distribution that diverges from SV2A mRNA patterns. Furthermore, we found a positive association between IQ and SV2A density in several higher-order cortical areas. This new resource will help advance our understanding of brain physiology and the pathogenesis of brain disorders, serving as a pivotal tool for future neuroscience research.

Copula linked parallel ICA jointly estimates linked structural and functional MRI brain networks

Different brain imaging methods provide valuable insights, and their combination enhances understanding of the brain. Existing fusion approaches typically use precomputed functional magnetic resonance imaging (fMRI) features, such as amplitude of low frequency fluctuations, regional homogeneity, or functional network connectivity while linking fMRI and structural MRI (sMRI). The fusion step typically ignores the detailed temporal information available in the complete 4D fMRI. Motivated by prior work showing covarying sMRI networks resemble resting fMRI networks, we introduce a new technique called copula linked parallel ICA (CLiP-ICA). This innovative method simultaneously estimates independent sources and an unmixing matrix for each modality while also linking spatial sources through a copula model. We tested the effectiveness of CLiP-ICA in both a simulation and a real-data using fMRI and sMRI data from an Alzheimer study. Results showed significant linkage in several domains including cerebellum, sensorimotor and default mode. In sum, we provide an approach to simultaneously estimate and link independent components of fMRI and sMRI while preserving temporal information.

Effects of Tasks on Functional Brain Connectivity Derived from Inter-Individual Correlations: Insights from Regional Homogeneity of Functional MRI Data

Research on brain functional connectivity often relies on intra-individual moment-to-moment correlations of functional brain activity, typically using techniques like functional MRI (fMRI). Inter-individual correlations are also employed on data from fMRI and positron emission tomography (PET). Many past studies have not specified tasks for participants, keeping them in an implicit "resting" condition. This lack of task specificity raises questions about how different tasks impact inter-individual correlation estimates. In our analysis of fMRI data from 100 unrelated participants, scanned during seven task conditions and in a resting state, we calculated Regional Homogeneity (ReHo) for each task as a regional measure of brain functions. We found that changes in ReHo due to different tasks were relatively small compared with the variations across brain regions. Cross-region variations of ReHo were highly correlated between different tasks. Similarly, whole-brain inter-individual correlation patterns were remarkably consistent across the tasks, showing correlations greater than 0.78. Changes in inter-individual correlations between tasks were primarily driven by connectivity in the visual, somatomotor, default mode network, and the interactions between them. The subtle yet statistically significant differences in functional connectivity may be linked to specific brain regions associated with the studied tasks. Future studies should consider task design when exploring inter-individual connectivity in specific brain systems.

Synaptic density patterns in early Alzheimer's disease assessed by independent component analysis

Synaptic loss is a primary pathology in Alzheimer's disease and correlates best with cognitive impairment as found in post-mortem studies. Previously, we observed in vivo reductions of synaptic density with [11C]UCB-J PET (radiotracer for synaptic vesicle protein 2A) throughout the neocortex and medial temporal brain regions in early Alzheimer's disease. In this study, we applied independent component analysis to synaptic vesicle protein 2A-PET data to identify brain networks associated with cognitive deficits in Alzheimer's disease in a blinded data-driven manner. [11C]UCB-J binding to synaptic vesicle protein 2A was measured in 38 Alzheimer's disease (24 mild Alzheimer's disease dementia and 14 mild cognitive impairment) and 19 cognitively normal participants. [11C]UCB-J distribution volume ratio values were calculated with a whole cerebellum reference region. Principal components analysis was first used to extract 18 independent components to which independent component analysis was then applied. Subject loading weights per pattern were compared between groups using Kruskal-Wallis tests. Spearman's rank correlations were used to assess relationships between loading weights and measures of cognitive and functional performance: Logical Memory II, Rey Auditory Verbal Learning Test-long delay, Clinical Dementia Rating sum of boxes and Mini-Mental State Examination. We observed significant differences in loading weights among cognitively normal, mild cognitive impairment and mild Alzheimer's disease dementia groups in 5 of the 18 independent components, as determined by Kruskal-Wallis tests. Only Patterns 1 and 2 demonstrated significant differences in group loading weights after correction for multiple comparisons. Excluding the cognitively normal group, we observed significant correlations between the loading weights for Pattern 1 (left temporal cortex and the cingulate gyrus) and Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019), Mini-Mental State Examination (r = 0.48, P = 0.0055) and Logical Memory II score (r = 0.44, P = 0.013). For Pattern 2 (temporal cortices), significant associations were demonstrated between its loading weights and Logical Memory II score (r = 0.34, P = 0.0384). Following false discovery rate correction, only the relationship between the Pattern 1 loading weights with Clinical Dementia Rating sum of boxes (r = -0.54, P = 0.0019) and Mini-Mental State Examination (r = 0.48, P = 0.0055) remained statistically significant. We demonstrated that independent component analysis could define coherent spatial patterns of synaptic density. Furthermore, commonly used measures of cognitive performance correlated significantly with loading weights for two patterns within only the mild cognitive impairment/mild Alzheimer's disease dementia group. This study leverages data-centric approaches to augment the conventional region-of-interest-based methods, revealing distinct patterns that differentiate between mild cognitive impairment and mild Alzheimer's disease dementia, marking a significant advancement in the field.

PET imaging of synaptic density in Parkinsonian disorders

Synaptic dysfunction and altered synaptic pruning are present in people with Parkinsonian disorders. Dopamine loss and alpha-synuclein accumulation, two hallmarks of Parkinson's disease (PD) pathology, contribute to synaptic dysfunction and reduced synaptic density in PD. Atypical Parkinsonian disorders are likely to have unique spatiotemporal patterns of synaptic density, differentiating them from PD. Therefore, quantification of synaptic density has the potential to support diagnoses, monitor disease progression, and treatment efficacy. Novel radiotracers for positron emission tomography which target the presynaptic vesicle protein SV2A have been developed to quantify presynaptic density. The radiotracers have successfully investigated synaptic density in preclinical models of PD and people with Parkinsonian disorders. Therefore, this review will summarize the preclinical and clinical utilization of SV2A radiotracers in people with Parkinsonian disorders. We will evaluate how SV2A abundance is associated with other imaging modalities and the considerations for interpreting SV2A in Parkinsonian pathology.

Changes in microstructural similarity of hippocampal subfield circuits in pathological cognitive aging

The hippocampal networks support multiple cognitive functions and may have biological roles and functions in pathological cognitive aging (PCA) and its associated diseases, which have not been explored. In the current study, a total of 116 older adults with 39 normal controls (NC) (mean age: 52.3 ± 13.64 years; 16 females), 39 mild cognitive impairment (MCI) (mean age: 68.15 ± 9.28 years, 14 females), and 38 dementia (mean age: 73.82 ± 8.06 years, 8 females) were included. The within-hippocampal subfields and the cortico-hippocampal circuits were assessed via a micro-structural similarity network approach using T1w/T2w ratio and regional gray matter tissue probability maps, respectively. An analysis of covariance was conducted to identify between-group differences in structural similarities among hippocampal subfields. The partial correlation analyses were performed to associate changes in micro-structural similarities with cognitive performance in the three groups, controlling the effect of age, sex, education, and cerebral small-vessel disease. Compared with the NC, an altered T1w/T2w ratio similarity between left CA3 and left subiculum was observed in the mild cognitive impairment (MCI) and dementia. The left CA3 was the most impaired region correlated with deteriorated cognitive performance. Using these regions as seeds for GM similarity comparisons between hippocampal subfields and cortical regions, group differences were observed primarily between the left subiculum and several cortical regions. By utilizing T1w/T2w ratio as a proxy measure for myelin content, our data suggest that the imbalanced synaptic weights within hippocampal CA3 provide a substrate to explain the abnormal firing characteristics of hippocampal neurons in PCA. Furthermore, our work depicts specific brain structural characteristics of normal and pathological cognitive aging and suggests a potential mechanism for cognitive aging heterogeneity.

In vivo imaging of synaptic density in neurodegenerative disorders with positron emission tomography: A systematic review

Positron emission tomography (PET) with radiotracers that bind to synaptic vesicle glycoprotein 2 A (SV2A) enables quantification of synaptic density in the living human brain. Assessing the regional distribution and severity of synaptic density loss will contribute to our understanding of the pathological processes that precede atrophy in neurodegeneration. In this systematic review, we provide a discussion of in vivo SV2A PET imaging research for quantitative assessment of synaptic density in various dementia conditions: amnestic Mild Cognitive Impairment and Alzheimer's disease, Frontotemporal dementia, Progressive supranuclear palsy and Corticobasal degeneration, Parkinson's disease and Dementia with Lewy bodies, Huntington's disease, and Spinocerebellar Ataxia. We discuss the main findings concerning group differences and clinical-cognitive correlations, and explore relations between SV2A PET and other markers of pathology. Additionally, we touch upon synaptic density in healthy ageing and outcomes of radiotracer validation studies. Studies were identified on PubMed and Embase between 2018 and 2023; last searched on the 3rd of July 2023. A total of 36 studies were included, comprising 5 on normal ageing, 21 clinical studies, and 10 validation studies. Extracted study characteristics were participant details, methodological aspects, and critical findings. In summary, the small but growing literature on in vivo SV2A PET has revealed different spatial patterns of synaptic density loss among various neurodegenerative disorders that correlate with cognitive functioning, supporting the potential role of SV2A PET imaging for differential diagnosis. SV2A PET imaging shows tremendous capability to provide novel insights into the aetiology of neurodegenerative disorders and great promise as a biomarker for synaptic density reduction. Novel directions for future synaptic density research are proposed, including (a) longitudinal imaging in larger patient cohorts of preclinical dementias, (b) multi-modal mapping of synaptic density loss onto other pathological processes, and (c) monitoring therapeutic responses and assessing drug efficacy in clinical trials.

Neuroimaging Biomarkers in Parkinson's Disease

Idiopathic Parkinson's disease (PD) is a neurodegenerative disorder that affects multiple systems in the body and is characterized by a variety of motor and non-motor (e.g., psychiatric, autonomic) symptoms. As the fastest growing neurological disorder expected to affect over 12 million people globally by 2040 (Dorsey, Bloem JAMA Neurol 75(1):9-10. https://doi.org/10.1001/jamaneurol.2017.3299 . PMID: 29131880, 2018), PD poses an enormous individual and public health burden. Currently, there are no therapies that can slow down the disease progression in PD, and existing therapies are limited to symptomatic treatment. Importantly, people in the prodromal phase who are at high risk of developing PD can now be identified, which makes disease prevention an achievable goal. An in-depth understanding of the pathological processes in PD is crucial for prevention and treatment development. Advanced multimodal neuroimaging techniques provide unique biomarkers that can further our understanding of PD at multiple levels ranging from neurotransmitters to neural networks. These neuroimaging biomarkers also have value in clinical application, for example, in the differential diagnosis of PD. As the field continues to advance, neuroimaging biomarkers are expected to become more specific, more widely accessible, and can be readily incorporated into translational research for treatment development in PD.

Synaptic density affects clinical severity via network dysfunction in syndromes associated with frontotemporal lobar degeneration

There is extensive synaptic loss from frontotemporal lobar degeneration, in preclinical models and human in vivo and post mortem studies. Understanding the consequences of synaptic loss for network function is important to support translational models and guide future therapeutic strategies. To examine this relationship, we recruited 55 participants with syndromes associated with frontotemporal lobar degeneration and 24 healthy controls. We measured synaptic density with positron emission tomography using the radioligand [11C]UCB-J, which binds to the presynaptic vesicle glycoprotein SV2A, neurite dispersion with diffusion magnetic resonance imaging, and network function with task-free magnetic resonance imaging functional connectivity. Synaptic density and neurite dispersion in patients was associated with reduced connectivity beyond atrophy. Functional connectivity moderated the relationship between synaptic density and clinical severity. Our findings confirm the importance of synaptic loss in frontotemporal lobar degeneration syndromes, and the resulting effect on behaviour as a function of abnormal connectivity.

A new framework for metabolic connectivity mapping using bolus [18F]FDG PET and kinetic modeling

Metabolic connectivity (MC) has been previously proposed as the covariation of static [18F]FDG PET images across participants, i.e., across-individual MC (ai-MC). In few cases, MC has been inferred from dynamic [18F]FDG signals, i.e., within-individual MC (wi-MC), as for resting-state fMRI functional connectivity (FC). The validity and interpretability of both approaches is an important open issue. Here we reassess this topic, aiming to 1) develop a novel wi-MC methodology; 2) compare ai-MC maps from standardized uptake value ratio (SUVR) vs. [18F]FDG kinetic parameters fully describing the tracer behavior (i.e., Ki, K1, k3); 3) assess MC interpretability in comparison to structural connectivity and FC. We developed a new approach based on Euclidean distance to calculate wi-MC from PET time-activity curves. The across-individual correlation of SUVR, Ki, K1, k3 produced different networks depending on the chosen [18F]FDG parameter (k3 MC vs. SUVR MC, r = 0.44). We found that wi-MC and ai-MC matrices are dissimilar (maximum r = 0.37), and that the match with FC is higher for wi-MC (Dice similarity: 0.47-0.63) than for ai-MC (0.24-0.39). Our analyses demonstrate that calculating individual-level MC from dynamic PET is feasible and yields interpretable matrices that bear similarity to fMRI FC measures.

Effects of escitalopram on synaptic density in the healthy human brain: a randomized controlled trial

Selective serotonin reuptake inhibitors (SSRIs) are widely used for treating neuropsychiatric disorders. However, the exact mechanism of action and why effects can take several weeks to manifest is not clear. The hypothesis of neuroplasticity is supported by preclinical studies, but the evidence in humans is limited. Here, we investigate the effects of the SSRI escitalopram on presynaptic density as a proxy for synaptic plasticity. In a double-blind placebo-controlled study (NCT04239339), 32 healthy participants with no history of psychiatric or cognitive disorders were randomized to receive daily oral dosing of either 20 mg escitalopram (n = 17) or a placebo (n = 15). After an intervention period of 3-5 weeks, participants underwent a [11C]UCB-J PET scan (29 with full arterial input function) to quantify synaptic vesicle glycoprotein 2A (SV2A) density in the hippocampus and the neocortex. Whereas we find no statistically significant group difference in SV2A binding after an average of 29 (range: 24-38) days of intervention, our secondary analyses show a time-dependent effect of escitalopram on cerebral SV2A binding with positive associations between [11C]UCB-J binding and duration of escitalopram intervention. Our findings suggest that brain synaptic plasticity evolves over 3-5 weeks in healthy humans following daily intake of escitalopram. This is the first in vivo evidence to support the hypothesis of neuroplasticity as a mechanism of action for SSRIs in humans and it offers a plausible biological explanation for the delayed treatment response commonly observed in patients treated with SSRIs. While replication is warranted, these results have important implications for the design of future clinical studies investigating the neurobiological effects of SSRIs.

Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease

Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.

A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies

Magnetic resonance imaging (MRI) of the brain is commonly used to detect where chronic and active lesions are in multiple sclerosis (MS). MRI is also extensively used as a tool to calculate and extrapolate brain health by way of volumetric analysis or advanced imaging techniques. In MS patients, psychiatric symptoms are common comorbidities, with depression being the main one. Even though these symptoms are a major determinant of quality of life in MS, they are often overlooked and undertreated. There has been evidence of bidirectional interactions between the course of MS and comorbid psychiatric symptoms. In order to mitigate disability progression in MS, treating psychiatric comorbidities should be investigated and optimized. New research for the prediction of disease states or phenotypes of disability have advanced, primarily due to new technologies and a better understanding of the aging brain.

Brain connectomics: time for a molecular imaging perspective?

In the past two decades brain connectomics has evolved into a major concept in neuroscience. However, the current perspective on brain connectivity and how it underpins brain function relies mainly on the hemodynamic signal of functional magnetic resonance imaging (MRI). Molecular imaging provides unique information inaccessible to MRI-based and electrophysiological techniques. Thus, positron emission tomography (PET) has been successfully applied to measure neural activity, neurotransmission, and proteinopathies in normal and pathological cognition. Here, we position molecular imaging within the brain connectivity framework from the perspective of timeliness, validity, reproducibility, and resolution. We encourage the neuroscientific community to take an integrative approach whereby MRI-based, electrophysiological techniques, and molecular imaging contribute to our understanding of the brain connectome.

Linking resting-state network fluctuations with systems of coherent synaptic density: A multimodal fMRI and 11C-UCB-J PET study

Introduction: Resting-state network (RSN) connectivity is a widely used measure of the brain's functional organization in health and disease; however, little is known regarding the underlying neurophysiology of RSNs. The aim of the current study was to investigate associations between RSN connectivity and synaptic density assessed using the synaptic vesicle glycoprotein 2A radioligand 11C-UCB-J PET. Methods: Independent component analyses (ICA) were performed on resting-state fMRI and PET data from 34 healthy adult participants (16F, mean age: 46 ± 15 years) to identify a priori RSNs of interest (default-mode, right frontoparietal executive-control, salience, and sensorimotor networks) and select sources of 11C-UCB-J variability (medial prefrontal, striatal, and medial parietal). Pairwise correlations were performed to examine potential intermodal associations between the fractional amplitude of low-frequency fluctuations (fALFF) of RSNs and subject loadings of 11C-UCB-J source networks both locally and along known anatomical and functional pathways. Results: Greater medial prefrontal synaptic density was associated with greater fALFF of the anterior default-mode, posterior default-mode, and executive-control networks. Greater striatal synaptic density was associated with greater fALFF of the anterior default-mode and salience networks. Post-hoc mediation analyses exploring relationships between aging, synaptic density, and RSN activity revealed a significant indirect effect of greater age on fALFF of the anterior default-mode network mediated by the medial prefrontal 11C-UCB-J source. Discussion: RSN functional connectivity may be linked to synaptic architecture through multiple local and circuit-based associations. Findings regarding healthy aging, lower prefrontal synaptic density, and lower default-mode activity provide initial evidence of a neurophysiological link between RSN activity and local synaptic density, which may have relevance in neurodegenerative and psychiatric disorders.

PET imaging of animal models with depressive-like phenotypes

Major depressive disorder is a growing and poorly understood pathology. Due to technical and ethical limitations, a significant proportion of the research on depressive disorders cannot be performed on patients, but needs to be investigated in animal paradigms. Over the years, animal studies have provided new insight in the mechanisms underlying depression. Several of these studies have used PET imaging for the non-invasive and longitudinal investigation of the brain physiology. This review summarises the findings of preclinical PET imaging in different experimental paradigms of depression and compares these findings with observations from human studies. Preclinical PET studies in animal models of depression can be divided into three main different approaches: (a) investigation of glucose metabolism as a biomarker for regional and network involvement, (b) evaluation of the availability of different neuroreceptor populations associated with depressive phenotypes, and (c) monitoring of the inflammatory response in phenotypes of depression. This review also assesses the relevance of the use of PET imaging techniques in animal paradigms for the understanding of specific aspects of the depressive-like phenotypes, in particular whether it might contribute to achieve a more detailed characterisation of the clinical depressive phenotypes for the development of new therapies for depression.

Identification of pre-synaptic density networks using [11C]UCB-J PET imaging and ICA in mice

BACKGROUND

Synaptic vesicle glycoprotein 2A (SV2A) is a vesicle glycoprotein involved in neurotransmitter release. SV2A is located on the pre-synaptic terminals of neurons and visualized using the radioligand [¹¹C]UCB-J and positron emission tomography (PET) imaging. Thus, SV2A PET imaging can provide a proxy for pre-synaptic density in health and disease. This study aims to apply independent component analysis (ICA) to SV2A PET data acquired in mice to identify pre-synaptic density networks (pSDNs), explore how ageing affects these pSDNs, and determine the impact of a neurological disorder on these networks.

METHODS

We used [¹¹C]UCB-J PET imaging data (n = 135) available at different ages (3, 7, 10, and 16 months) in wild-type (WT) C57BL/6J mice and in diseased mice (mouse model of Huntington's disease, HD) with reported synaptic deficits. First, ICA was performed on a healthy dataset after it was split into two equal-sized samples (n = 36 each) and the analysis was repeated 50 times in different partitions. We tested different model orders (8, 12, and 16) and identified the pSDNs. Next, we investigated the effect of age on the loading weights of the identified pSDNs. Additionally, the identified pSDNs were compared to those of diseased mice to assess the impact of disease on each pSDNs.

RESULTS

Model order 12 resulted in the preferred choice to provide six reliable and reproducible independent components (ICs) as supported by the cluster-quality index (I_Q) and regression coefficients (β) values. Temporal analysis showed age-related statistically significant changes on the loading weights in four ICs. ICA in an HD model revealed a statistically significant disease-related effect on the loading weights in several pSDNs in line with the progression of the disease.

CONCLUSION

This study validated the use of ICA on SV2A PET data acquired with [¹¹C]UCB-J for the identification of cerebral pre-synaptic density networks in mice in a rigorous and reproducible manner. Furthermore, we showed that different pSDNs change with age and are affected in a disease condition. These findings highlight the potential value of ICA in understanding pre-synaptic density networks in the mouse brain.

Intra and inter: Alterations in functional brain resting-state networks in patients with functional constipation

Background

Functional constipation (FCon), is a symptom-based functional gastrointestinal disorder without an organic etiology and altering brain structure and function. However, previous studies mainly focused on isolated brain regions involved in brain plasticity. Therefore, little is known about the altered large-scale interaction of brain networks in FCon.

Methods

For this study, we recruited 20 patients with FCon and 20 healthy controls. We used group independent component analysis to identify resting-state networks (RSNs) and documented intra- and inter-network alterations in the RSNs of the patients with FCon.

Results

We found 14 independent RSNs. Differences in the intra-networks included decreased activities in the bilateral caudate of RSN 3 (strongly related to emotional and autonomic processes) and decreased activities in the left precuneus of RSN 10 (default mode network). Notably, the patients with FCon exhibited significantly decreased interactive connectivity between RSNs, mostly involving the connections to the visual perception network (RSN 7–9).

Conclusion

Compared with healthy controls, patients with FCon had extensive brain plastic changes within and across related RSNs. Furthermore, the macroscopic brain alterations in FCon were associated with interoceptive abilities, emotion processing, and sensorimotor control. These insights could therefore lead to the development of new treatment strategies for FCon.

Concise Review: Recent advances in neuroimaging techniques to assist clinical trials on cell-based therapies in neurodegenerative diseases

Neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are progressive disorders for which a curative therapy is still lacking. Cell-based therapy aims at replacing dysfunctional cellular populations by repairing damaged tissue and by enriching the microenvironment of selective brain areas, and thus constitutes a promising disease-modifying treatment of neurodegenerative diseases. Scientific research has engineered a wide range of human-derived cellular populations to help overcome some of the logistical, safety, and ethical issues associated with this approach. Open-label studies and clinical trials in human participants have employed neuroimaging techniques, such as positron emission tomography (PET) and magnetic resonance imaging (MRI), to assess the success of the transplantation, to evaluate the functional integration of the implanted tissue into the host environment and to understand the pathophysiological changes associated with the therapy. Neuroimaging has constituted an outcome measure of large, randomized clinical trials, and has given answers to clarify the pathophysiology underlying some of the complications linked with this therapy. Novel PET radiotracers and MRI sequences for the staging of neurodegenerative diseases and to study alterations at molecular level significantly expands the translational potential of neuroimaging to assist pre-clinical and clinical research on cell-based therapy in these disorders. This concise review summarizes the current use of neuroimaging in human studies of cell-based replacement therapy and focuses on future application of PET and MRI techniques to evaluate the pathophysiology and treatment efficacy, as well as to aid patient selection and as an outcome measure to improve treatment success.

Data-driven analysis of kappa opioid receptor binding in major depressive disorder measured by positron emission tomography

Preclinical studies have implicated kappa opioid receptors (KORs) in stress responses and depression-related behaviors, but evidence from human studies is limited. Here we present results of a secondary analysis of data acquired using positron emission tomography (PET) with the KOR radiotracer [¹¹C]GR103545 in 10 unmedicated, currently depressed individuals with major depressive disorder (MDD; 32.6 ± 6.5 years, 5 women) and 13 healthy volunteers (34.8 ± 10 years, 6 women). Independent component analysis was performed to identify spatial patterns of coherent variance in KOR binding (tracer volume of distribution, V_T) across all subjects. Expression of each component was compared between groups and relationships to symptoms were explored using the 17-item Hamilton Depression Rating Scale (HDRS). Three components of variation in KOR availability across ROIs were identified, spatially characterized by [¹¹C]GR103545 V_T in (1) bilateral frontal lobe; (2) occipital and parietal cortices, right hippocampus, and putamen; and (3) right anterior cingulate, right superior frontal gyrus and insula, coupled to negative loading in left middle cingulate. In MDD patients, component 3 was negatively associated with symptom severity on the HDRS (r = -0.85, p = 0.0021). There were no group-wise differences in expression of any component between patients and controls. These preliminary findings suggest that KOR signaling in cortical regions relevant to depression, particularly right anterior cingulate, could reflect MDD pathophysiology.