Mapping neurotransmitter systems to the structural and functional organization of the human neocortex

Neural Correlates of Early-Life Urbanization and Their Spatial Relationships with Gene Expression, Neurotransmitter, and Behavioral Domain Atlases

Previous neuroimaging research has established associations between urban exposure during early life and alterations in brain function and structure. However, the molecular mechanisms and behavioral relevance of these associations remain largely unknown. Here, we aimed to address this question using a combined analysis of multimodal data. Initially, we calculated amplitude of low-frequency fluctuations (ALFF) and gray matter volume (GMV) using resting-state functional and structural MRI to investigate their associations with early-life urbanization in a large sample of 511 healthy young adults. Then, we examined the spatial relationships of the identified neural correlates of early-life urbanization with gene expression, neurotransmitter, and behavioral domain atlases. Results showed that higher early-life urbanization scores were correlated with increased ALFF of the right fusiform gyrus and decreased GMV of the left dorsal medial prefrontal cortex and left precuneus. Remarkably, the identified neural correlates of early-life urbanization were spatially correlated with expression of gene categories primarily involving immune system process, signal transduction, and cellular metabolic process. Concurrently, there were significant associations between the neural correlates and specific neurotransmitter systems including dopamine, acetylcholine, and serotonin. Finally, we found that the ALFF correlates were associated with behavioral terms including "perception," "sensory," "cognitive control," and "reasoning." Apart from expanding existing knowledge of early-life urban environmental risk for mental disorders and health in general, our findings may contribute to an emerging framework for integrating social science, neuroscience, genetics, and public policy to respond to the major health challenge of world urbanization.

Pretreatment Brain White Matter Integrity Associated With Neuropathic Pain Relief and Changes in Temporal Summation of Pain Following Ketamine

Neuropathic pain (NP) is a prevalent condition often associated with heightened pain responsiveness suggestive of central sensitization. Neuroimaging biomarkers of treatment outcomes may help develop personalized treatment strategies, but white matter (WM) properties have been underexplored for this purpose. Here we assessed whether WM pathways of the default mode network (DMN: medial prefrontal cortex [mPFC], posterior cingulate cortex, and precuneus) and descending pain modulation system (periaqueductal gray [PAG]) are associated with ketamine analgesia and attenuated temporal summation of pain (TSP, reflecting central sensitization) in NP. We used a fixel-based analysis of diffusion-weighted imaging data to evaluate WM microstructure (fiber density [FD]) and macrostructure (fiber bundle cross-section) within the DMN and mPFC-PAG pathways in 70 individuals who underwent magnetic resonance imaging and TSP testing; 35 with NP who underwent ketamine treatment and 35 age- and sex-matched pain-free individuals. Individuals with NP were assessed before and 1 month after treatment; those with ≥30% pain relief were considered responders (n = 18), or otherwise as nonresponders (n = 17). We found that WM structure within the DMN and mPFC-PAG pathways did not differentiate responders from nonresponders. However, pretreatment FD in the anterior limb of the internal capsule correlated with pain relief (r=.48). Moreover, pretreatment FD in the DMN (left mPFC-precuneus/posterior cingulate cortex; r=.52) and mPFC-PAG (r=.42) negatively correlated with changes in TSP. This suggests that WM microstructure in the DMN and mPFC-PAG pathway is associated with the degree to which ketamine reduces central sensitization. Thus, fixel metrics of WM structure may hold promise to predict ketamine NP treatment outcomes. PERSPECTIVE: We used advanced fixel-based analyses of MRI diffusion-weighted imaging data to identify pretreatment WM microstructure associated with ketamine outcomes, including analgesia and markers of attenuated central sensitization. Exploring associations between brain structure and treatment outcomes could contribute to a personalized approach to treatment for individuals with NP.

Chemoarchitectural signatures of subcortical shape alterations in generalized epilepsy

Genetic generalized epilepsies (GGE) exhibit widespread morphometric alterations in the subcortical structures. Subcortical structures are essential for understanding GGE pathophysiology, but their fine-grained morphological diversity has yet to be comprehensively investigated. Furthermore, the relationships between macroscale morphological disturbances and microscale molecular chemoarchitectures are unclear. High-resolution structural images were acquired from patients with GGE (n = 97) and sex- and age-matched healthy controls (HCs, n = 184). Individual measurements of surface shape features (thickness and surface area) of seven bilateral subcortical structures were quantified. The patients and HCs were then compared vertex-wise, and shape anomalies were co-located with brain neurotransmitter profiles. We found widespread morphological alterations in GGE and prominent disruptions in the thalamus, putamen, and hippocampus. Shape area dilations were observed in the bilateral ventral, medial, and right dorsal thalamus, as well as the bilateral lateral putamen. We found that the shape area deviation pattern was spatially correlated with the norepinephrine transporter and nicotinic acetylcholine (Ach) receptor (α4β2) profiles, but a distinct association was seen in the muscarinic Ach receptor (M1). The findings provided a comprehensive picture of subcortical morphological disruptions in GGE, and further characterized the associated molecular mechanisms. This information may increase our understanding of the pathophysiology of GGE.

Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies

The human brain is organized as a complex, hierarchical network. However, the structural covariance patterns among brain regions and the underlying biological substrates of such covariance networks remain to be clarified. The present study proposed a novel individualized structural covariance network termed voxel-based texture similarity networks (vTSNs) based on 76 refined voxel-based textural features derived from structural magnetic resonance images. Validated in three independent longitudinal healthy cohorts (40, 23, and 60 healthy participants, respectively) with two common brain atlases, we found that the vTSN could robustly resolve inter-subject variability with high test-retest reliability. In contrast to the regional-based texture similarity networks (rTSNs) that calculate radiomic features based on region-of-interest information, vTSNs had higher inter- and intra-subject variability ratios and test-retest reliability in connectivity strength and network topological properties. Moreover, the Spearman correlation indicated a stronger association of the gene expression similarity network (GESN) with vTSNs than with rTSNs (vTSN: r = 0.600, rTSN: r = 0.433, z = 39.784, P < 0.001). Hierarchical clustering identified 3 vTSN subnets with differential association patterns with 13 coexpression modules, 16 neurotransmitters, 7 electrophysiology, 4 metabolism, and 2 large-scale structural and 4 functional organization maps. Moreover, these subnets had unique biological hierarchical organization from the subcortex-limbic system to the ventral neocortex and then to the dorsal neocortex. Based on 424 unrelated, qualified healthy subjects from the Human Connectome Project, we found that vTSNs could sensitively represent sex differences, especially for connections in the subcortex-limbic system and between the subcortex-limbic system and the ventral neocortex. Moreover, a multivariate variance component model revealed that vTSNs could explain a significant proportion of inter-subject behavioral variance in cognition (80.0 %) and motor functions (63.4 %). Finally, using 494 healthy adults (aged 19-80 years old) from the Southwest University Adult Lifespan Dataset, the Spearman correlation identified a significant association between aging and vTSN strength, especially within the subcortex-limbic system and between the subcortex-limbic system and the dorsal neocortex. In summary, our proposed vTSN is robust in uncovering individual variability and neurobiological brain processes, which can serve as biologically plausible measures for linking biological processes and human behavior.

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/β-hydrolase domain-containing 6 (ABHD6), β 1,3-N-acetylglucosaminyltransferase-9(β3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, β3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

Decreased inter-hemispheric cooperation in major depressive disorder and its association with neurotransmitter profiles

BACKGROUND

Inter-hemispheric cooperation is a prominent feature of the human brain, and previous neuroimaging studies have revealed aberrant inter-hemispheric cooperation patterns in patients with major depressive disorder (MDD). Typically, inter-hemispheric cooperation is examined by calculating the functional connectivity (FC) between each voxel in one hemisphere and its anatomical (structurally homotopic) counterpart in the opposite hemisphere. However, bilateral hemispheres are actually asymmetric in anatomy.

METHODS

In the present study, we utilized connectivity between functionally homotopic voxels (CFH) to investigate abnormal inter-hemispheric cooperation in 96 MDD patients compared to 173 age- and sex-matched healthy controls (HCs). In addition, we analyzed the spatial correlations between abnormal CFH and the density maps of 13 neurotransmitter receptors and transporters.

RESULTS

The CFH values in bilateral orbital frontal gyri and bilateral postcentral gyri were abnormally decreased in patients with MDD. Furthermore, these CFH abnormalities were correlated with clinical symptoms. In addition, the abnormal CFH pattern in MDD patients was spatially correlated with the distribution pattern of 5-HT1AR.

LIMITATIONS

drug effect; the cross-sectional research design precludes causal inferences; the neurotransmitter atlases selected were constructed from healthy individuals rather than MDD patients.

CONCLUSION

These findings characterized the abnormal inter-hemispheric cooperation in MDD using a novel method and the underlying neurotransmitter mechanism, which promotes our understanding of the pathophysiology of depression.

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.

Multimodal neuroimaging to characterize symptom-specific networks in movement disorders

Movement disorders, such as Parkinson's disease, essential tremor, and dystonia, are characterized by their predominant motor symptoms, yet diseases causing abnormal movement also encompass several other symptoms, including non-motor symptoms. Here we review recent advances from studies of brain lesions, neuroimaging, and neuromodulation that provide converging evidence on symptom-specific brain networks in movement disorders. Although movement disorders have traditionally been conceptualized as disorders of the basal ganglia, cumulative data from brain lesions causing parkinsonism, tremor and dystonia have now demonstrated that this view is incomplete. Several recent studies have shown that lesions causing a given movement disorder occur in heterogeneous brain locations, but disrupt common brain networks, which appear to be specific to each motor phenotype. In addition, findings from structural and functional neuroimaging in movement disorders have demonstrated that brain abnormalities extend far beyond the brain networks associated with the motor symptoms. In fact, neuroimaging findings in each movement disorder are strongly influenced by the constellation of patients' symptoms that also seem to map to specific networks rather than individual anatomical structures or single neurotransmitters. Finally, observations from deep brain stimulation have demonstrated that clinical changes, including both symptom improvement and side effects, are dependent on the modulation of large-scale networks instead of purely local effects of the neuromodulation. Combined, this multimodal evidence suggests that symptoms in movement disorders arise from distinct brain networks, encouraging multimodal imaging studies to better characterize the underlying symptom-specific mechanisms and individually tailor treatment approaches.

PET-based brain molecular connectivity in neurodegenerative disease

PURPOSE OF REVIEW

Molecular imaging has traditionally been used and interpreted primarily in the context of localized and relatively static neurochemical processes. New understanding of brain function and development of novel molecular imaging protocols and analysis methods highlights the relevance of molecular networks that co-exist and interact with functional and structural networks. Although the concept and evidence of disease-specific metabolic brain patterns has existed for some time, only recently has such an approach been applied in the neurotransmitter domain and in the context of multitracer and multimodal studies. This review briefly summarizes initial findings and highlights emerging applications enabled by this new approach.

RECENT FINDINGS

Connectivity based approaches applied to molecular and multimodal imaging have uncovered molecular networks with neurodegeneration-related alterations to metabolism and neurotransmission that uniquely relate to clinical findings; better disease stratification paradigms; an improved understanding of the relationships between neurochemical and functional networks and their related alterations, although the directionality of these relationships are still unresolved; and a new understanding of the molecular underpinning of disease-related alteration in resting-state brain activity.

SUMMARY

Connectivity approaches are poised to greatly enhance the information that can be extracted from molecular imaging. While currently mostly contributing to enhancing understanding of brain function, they are highly likely to contribute to the identification of specific biomarkers that will improve disease management and clinical care.

Compressed representation of brain genetic transcription

The architecture of the brain is too complex to be intuitively surveyable without the use of compressed representations that project its variation into a compact, navigable space. The task is especially challenging with high-dimensional data, such as gene expression, where the joint complexity of anatomical and transcriptional patterns demands maximum compression. The established practice is to use standard principal component analysis (PCA), whose computational felicity is offset by limited expressivity, especially at great compression ratios. Employing whole-brain, voxel-wise Allen Brain Atlas transcription data, here we systematically compare compressed representations based on the most widely supported linear and non-linear methods-PCA, kernel PCA, non-negative matrix factorisation (NMF), t-stochastic neighbour embedding (t-SNE), uniform manifold approximation and projection (UMAP), and deep auto-encoding-quantifying reconstruction fidelity, anatomical coherence, and predictive utility across signalling, microstructural, and metabolic targets, drawn from large-scale open-source MRI and PET data. We show that deep auto-encoders yield superior representations across all metrics of performance and target domains, supporting their use as the reference standard for representing transcription patterns in the human brain.

The Identification of Potential Anti-Depression/Anxiety Drug Targets by Stress-Induced Rat Brain Regional Proteome and Network Analyses

Depression and anxiety disorders are prevalent stress-related neuropsychiatric disorders and involve multiple molecular changes and dysfunctions across various brain regions. However, the specific and shared pathophysiological mechanisms occurring in these regions remain unclear. Previous research used a rat model of chronic mild stress (CMS) to segregate and identify depression-susceptible, anxiety-susceptible, and insusceptible groups; then the proteomes of six distinct brain regions (the hippocampus, prefrontal cortex, hypothalamus, pituitary, olfactory bulb, and striatum) were separately and quantitatively analyzed. To gain a comprehensive and systematic understanding of the molecular abnormalities, this study aimed to investigate and compare differential proteomics data from the six regions. Differentially expressed proteins (DEPs) were identified in between specific regions and across all regions and subjected to a series of bioinformatics analyses. Regional comparisons showed that stress-induced proteomic changes and corresponding gene ontology and pathway enrichments were largely distinct, attributable to differences in cell populations, protein compositions, and brain functions of these areas. Additionally, a notable degree of overlap in the significantly enriched terms was identified, potentially suggesting strong connections in the enrichment across different regions. Furthermore, intra-regional and inter-regional protein-protein interaction networks and drug-target-DEP networks were constructed. Integrated analysis of the three association networks in the six regions, along with the DisGeNET database, identified ten DEPs as potential targets for anti-depression/anxiety drugs. Collectively, these findings revealed commonalities and differences across different brain regions at the protein level induced by CMS, and identified several novel protein targets for the development of new therapeutics for depression and anxiety.

Transcriptional expression patterns of the cortical morphometric similarity network in progressive supranuclear palsy

BACKGROUND

It has been demonstrated that progressive supranuclear palsy (PSP) correlates with structural abnormalities in several distinct regions of the brain. However, whether there are changes in the morphological similarity network (MSN) and the relationship between changes in brain structure and gene expression remain largely unknown.

METHODS

We used two independent cohorts (discovery dataset: PSP: 51, healthy controls (HC): 82; replication dataset: PSP: 53, HC: 55) for MSN analysis and comparing the longitudinal changes in the MSN of PSP. Then, we applied partial least squares regression to determine the relationships between changes in MSN and spatial transcriptional features and identified specific genes associated with MSN differences in PSP. We further investigated the biological processes enriched in PSP-associated genes and the cellular characteristics of these genes, and finally, we performed an exploratory analysis of the relationship between MSN changes and neurotransmitter receptors.

RESULTS

We found that the MSN in PSP patients was mainly decreased in the frontal and temporal cortex but increased in the occipital cortical region. This difference is replicable. In longitudinal studies, MSN differences are mainly manifested in the frontal and parietal regions. Furthermore, the expression pattern associated with MSN changes in PSP involves genes implicated in astrocytes and excitatory and inhibitory neurons and is functionally enriched in neuron-specific biological processes related to synaptic signaling. Finally, we found that the changes in MSN were mainly negatively correlated with the levels of serotonin, norepinephrine, and opioid receptors.

CONCLUSIONS

These results have enhanced our understanding of the microscale genetic and cellular mechanisms responsible for large-scale morphological abnormalities in PSP patients, suggesting potential targets for future therapeutic trials.

Quantitative susceptibility mapping of multiple system atrophy and Parkinson's disease correlates with neurotransmitter reference maps

BACKGROUND

Multiple system atrophy (MSA) and Parkinson's disease (PD) are neurodegenerative disorders characterized by α-synuclein pathology, disrupted iron homeostasis and impaired neurochemical transmission. Considering the critical role of iron in neurotransmitter synthesis and transport, our study aims to identify distinct patterns of whole-brain iron accumulation in MSA and PD, and to elucidate the corresponding neurochemical substrates.

METHODS

A total of 122 PD patients, 58 MSA patients and 78 age-, sex-matched health controls underwent multi-echo gradient echo sequences and neurological evaluations. We conducted voxel-wise and regional analyses using quantitative susceptibility mapping to explore MSA or PD-specific alterations in cortical and subcortical iron concentrations. Spatial correlation approaches were employed to examine the topographical alignment of cortical iron accumulation patterns with normative atlases of neurotransmitter receptor and transporter densities. Furthermore, we assessed the associations between the colocalization strength of neurochemical systems and disease severity.

RESULTS

MSA patients exhibited increased susceptibility in the striatal, midbrain, cerebellar nuclei, as well as the frontal, temporal, occipital lobes, and anterior cingulate gyrus. In contrast, PD patients displayed elevated iron levels in the left inferior occipital gyrus, precentral gyrus, and substantia nigra. The excessive iron accumulation in MSA or PD correlated with the spatial distribution of cholinergic, noradrenaline, glutamate, serotonin, cannabinoids, and opioid neurotransmitters, and the degree of this alignment was related to motor deficits.

CONCLUSIONS

Our findings provide evidence of the interaction between iron accumulation and non-dopamine neurotransmitters in the pathogenesis of MSA and PD, which inspires research on potential targets for pharmacotherapy.

A connectome-wide association study of altered functional connectivity in schizophrenia based on resting-state fMRI

BACKGROUND

Aberrant resting-state functional connectivity is a neuropathological feature of schizophrenia (SCZ). Prior investigations into functional connectivity abnormalities have primarily employed seed-based connectivity analysis, necessitating predefined seed locations. To address this limitation, a data-driven multivariate method known as connectome-wide association study (CWAS) has been proposed for exploring whole-brain functional connectivity.

METHODS

We conducted a CWAS analysis involving 46 patients with SCZ and 40 age- and sex-matched healthy controls. Multivariate distance matrix regression (MDMR) was utilized to identify key nodes in the brain. Subsequently, we conducted a follow-up seed-based connectivity analysis to elucidate specific connectivity patterns between regions of interest (ROIs). Additionally, we explored the spatial correlation between changes in functional connectivity and underlying molecular architectures by examining correlations between neurotransmitter/transporter distribution densities and functional connectivity.

RESULTS

MDMR revealed the right medial frontal gyrus and the left calcarine sulcus as two key nodes. Follow-up analysis unveiled hypoconnectivity between the right medial frontal superior gyrus and the right fusiform gyrus, as well as hypoconnectivity between the left calcarine sulcus and the right lingual gyrus in SCZ. Notably, a significant association between functional connectivity strength and positive symptom severity was identified. Furthermore, altered functional connectivity patterns suggested potential dysfunctions in the dopamine, serotonin, and gamma-aminobutyric acid systems.

CONCLUSIONS

This study elucidated reduced functional connectivity both within and between the medial frontal regions and the occipital cortex in patients with SCZ. Moreover, it indicated potential alterations in molecular architecture, thereby expanding current knowledge regarding neurobiological changes associated with SCZ.

Eigenvector centrality mapping reveals volatility of functional brain dynamics in anti-NMDA receptor encephalitis

BACKGROUND

Anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) causes long-lasting cognitive deficits associated with altered functional connectivity. Eigenvector centrality (EC) mapping represents a powerful new method for data-driven voxel-wise and time-resolved estimation of network importance - beyond changes in classical 'static' functional connectivity.

METHODS

To assess changes in functional brain network organization, we applied EC mapping in 73 patients with NMDARE and 73 matched healthy controls. Areas with significant group differences were further investigated using (i) spatial clustering analyses, (ii) time series correlation to assess synchronicity between the hippocampus and cortical brain regions, and (iii) correlation with cognitive and clinical parameters.

RESULTS

Dynamic, time-resolved EC showed significantly higher variability in 13 cortical areas (p(FWE)<0.05) in patients with NMDARE compared to HC. Areas with dynamic EC group differences were spatially organized in centrality clusters resembling resting-state networks. Importantly, variability of dynamic EC in the frontotemporal cluster was associated with impaired verbal episodic memory in patients (r=-0.25, p=0.037). EC synchronicity between the hippocampus and the medial prefrontal cortex was reduced in patients compared to HC (p(FWE)<0.05, t(max)=3.76), and associated with verbal episodic memory in patients (r=0.28, p=0.019). Static EC analyses showed group differences in only one brain region (left intracalcarine cortex).

CONCLUSIONS

Widespread changes in network dynamics and reduced hippocampal-medial prefrontal synchronicity were associated with verbal episodic memory deficits and may thus represent a functional neural correlate of cognitive dysfunction in NMDARE. Importantly, dynamic EC detected substantially more network alterations than traditional static approaches, highlighting the potential of this method to explain long-term deficits in NMDARE.

A synergistic workspace for human consciousness revealed by Integrated Information Decomposition

How is the information-processing architecture of the human brain organised, and how does its organisation support consciousness? Here, we combine network science and a rigorous information-theoretic notion of synergy to delineate a 'synergistic global workspace', comprising gateway regions that gather synergistic information from specialised modules across the human brain. This information is then integrated within the workspace and widely distributed via broadcaster regions. Through functional MRI analysis, we show that gateway regions of the synergistic workspace correspond to the human brain's default mode network, whereas broadcasters coincide with the executive control network. We find that loss of consciousness due to general anaesthesia or disorders of consciousness corresponds to diminished ability of the synergistic workspace to integrate information, which is restored upon recovery. Thus, loss of consciousness coincides with a breakdown of information integration within the synergistic workspace of the human brain. This work contributes to conceptual and empirical reconciliation between two prominent scientific theories of consciousness, the Global Neuronal Workspace and Integrated Information Theory, while also advancing our understanding of how the human brain supports consciousness through the synergistic integration of information.

Electroencephalography connectome changes in chronic insomnia disorder are correlated with neurochemical signatures

STUDY OBJECTIVES

This study aimed to investigate the alterations in resting-state electroencephalography (EEG) global brain connectivity (GBC) in patients with chronic insomnia disorder (CID) and to explore the correlation between macroscale connectomic variances and microscale neurotransmitter distributions.

METHODS

We acquired 64-channel EEG from 35 female CID patients and 34 healthy females. EEG signals were source-localized using individual brain anatomy and orthogonalized to mitigate volume conduction. Correlation coefficients between band-limited source-space power envelopes of the DK 68 atlas were computed and averaged across regions to determine specific GBC values. A support vector machine (SVM) classifier utilizing GBC features was employed to differentiate CID patients from controls. We further used Neurosynth and a 3D atlas of neurotransmitter receptors/transporters to assess the cognitive functions and neurotransmitter landscape associated with CID cortical abnormality maps, respectively.

RESULTS

CID patients exhibited elevated GBC within the medial prefrontal cortex and limbic cortex, particularly at the gamma carrier frequency, compared to controls (pFDR < .05). GBC patterns were found to effectively distinguish CID patients from controls with a precision of 90.8% in the SVM model. The cortical abnormality maps were significantly correlated with meta-analytic terms like "cognitive control" and "emotion regulation." Notably, GBC patterns were associated with neurotransmitter profiles (pspin < .05), with neurotransmitter systems such as norepinephrine, dopamine, and serotonin making significant contributions.

CONCLUSIONS

This work characterizes the EEG connectomic profile of CID, facilitating the cost-effective clinical translation of EEG-derived markers. Additionally, the linkage between GBC patterns and neurotransmitter distribution offers promising avenues for developing targeted treatment strategies for CID.

Excitation/Inhibition balance relates to cognitive function and gene expression in temporal lobe epilepsy: a high density EEG assessment with aperiodic exponent

Patients with epilepsy are characterized by a dysregulation of excitation/inhibition balance (E/I). The assessment of E/I may inform clinicians during the diagnosis and therapy management, even though it is rarely performed. An accessible measure of the E/I of the brain represents a clinically relevant feature. Here, we exploited the exponent of the aperiodic component of the power spectrum of the electroencephalography (EEG) signal, as a non-invasive and cost-effective proxy of the E/I balance. We recorded resting-state activity with high-density EEG from 67 patients with temporal lobe epilepsy and 35 controls. We extracted the exponent of the aperiodic fit of the power spectrum from source-reconstructed EEG and tested differences between patients with epilepsy and controls. Spearman's correlation was performed between the exponent and clinical variables (age of onset, epilepsy duration and neuropsychology) and cortical expression of epilepsy-related genes derived from the Allen Human Brain Atlas. Patients with temporal lobe epilepsy showed a significantly larger exponent, corresponding to inhibition-directed E/I balance, in bilateral frontal and temporal regions. Lower E/I in the left entorhinal and bilateral dorsolateral prefrontal cortices corresponded to a lower performance of short-term verbal memory. Limited to patients with temporal lobe epilepsy, we detected a significant correlation between the exponent and the cortical expression of GABRA1, GRIN2A, GABRD, GABRG2, KCNA2 and PDYN genes. EEG aperiodic exponent maps the E/I balance non-invasively in patients with epilepsy and reveals a close relationship between altered E/I patterns, cognition and genetics.

Multi-scale brain attributes contribute to the distribution of diffuse glioma subtypes

Gliomas are primary brain tumors and are among the most malignant types. Adult-type diffuse gliomas can be classified based on their histological and molecular signatures as IDH-wildtype glioblastoma, IDH-mutant astrocytoma, and IDH-mutant and 1p/19q-codeleted oligodendroglioma. Recent studies have shown that each subtype of glioma has its own specific distribution pattern. However, the mechanisms underlying the specific distributions of glioma subtypes are not entirely clear despite partial explanations such as cell origin. To investigate the impact of multi-scale brain attributes on glioma distribution, we constructed cumulative frequency maps for diffuse glioma subtypes based on T1w structural images and evaluated the spatial correlation between tumor frequency and diverse brain attributes, including postmortem gene expression, functional connectivity metrics, cerebral perfusion, glucose metabolism, and neurotransmitter signaling. Regression models were constructed to evaluate the contribution of these factors to the anatomic distribution of different glioma subtypes. Our findings revealed that the three different subtypes of gliomas had distinct distribution patterns, showing spatial preferences toward different brain environmental attributes. Glioblastomas were especially likely to occur in regions enriched with synapse-related pathways and diverse neurotransmitter receptors. Astrocytomas and oligodendrogliomas preferentially occurred in areas enriched with genes associated with neutrophil-mediated immune responses. The functional network characteristics and neurotransmitter distribution also contributed to oligodendroglioma distribution. Our results suggest that different brain transcriptomic, neurotransmitter, and connectomic attributes are the factors that determine the specific distributions of glioma subtypes. These findings highlight the importance of bridging diverse scales of biological organization when studying neurological dysfunction.

Pharmacological Elevation of Catecholamine Levels Improves Perceptual Decisions, But Not Metacognitive Insight

Perceptual decisions are often accompanied by a feeling of decision confidence. Where the parietal cortex is known for its crucial role in shaping such perceptual decisions, metacognitive evaluations are thought to additionally rely on the (pre)frontal cortex. Because of this supposed neural differentiation between these processes, perceptual and metacognitive decisions may be divergently affected by changes in internal (e.g., attention, arousal) and external (e.g., task and environmental demands) factors. Although intriguing, causal evidence for this hypothesis remains scarce. Here, we investigated the causal effect of two neuromodulatory systems on behavioral and neural measures of perceptual and metacognitive decision-making. Specifically, we pharmacologically elevated levels of catecholamines (with atomoxetine) and acetylcholine (with donepezil) in healthy adult human participants performing a visual discrimination task in which we gauged decision confidence, while electroencephalography was measured. Where cholinergic effects were not robust, catecholaminergic enhancement improved perceptual sensitivity, while at the same time leaving metacognitive sensitivity unaffected. Neurally, catecholaminergic elevation did not affect sensory representations of task-relevant visual stimuli but instead enhanced well-known decision signals measured over the centroparietal cortex, reflecting the accumulation of sensory evidence over time. Crucially, catecholaminergic enhancement concurrently impoverished neural markers measured over the frontal cortex linked to the formation of metacognitive evaluations. Enhanced catecholaminergic neuromodulation thus improves perceptual but not metacognitive decision-making.

Trim46 knockout impaired neuronal architecture and caused hypoactive behavior in rats

BACKGROUND

Tripartite motif (TRIM46) is a relatively novel protein that belongs to tripartite motif family. TRIM46 organizes parallel microtubule arrays on the axons, which are important for neuronal polarity and axonal function. TRIM46 is highly expressed in the brain, but its biological function in adults has not yet been determined.

RESULTS

Trim46 knockout (KO) rat line was established using CRISPR/cas9. Trim46 KO rats had smaller hippocampus sizes, fewer neuronal dendritic arbors and dendritic spines, and shorter and more distant axon initial segment. Furthermore, the protein interaction between endogenous TRIM46 and FK506 binding protein 5 (FKBP5) in brain tissues was determined; Trim46 KO increased hippocampal FKBP5 protein levels and decreased hippocampal protein kinase B (Akt) phosphorylation, gamma-aminobutyric acid type A receptor subunit alpha1 (GABRA1) and glutamate ionotropic receptor NMDA type subunit 1 (NMDAR1) protein levels. Trim46 KO rats exhibited hypoactive behavioral changes such as reduced spontaneous activity, social interaction, sucrose preference, impaired prepulse inhibition (PPI), and short-term reference memory.

CONCLUSIONS

These results demonstrate the significant impact of Trim46 KO on brain structure and behavioral function. This study revealed a novel potential association of TRIM46 with dendritic development and neuropsychiatric behavior, providing new insights into the role of TRIM46 in the brain.

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.

Brain structural changes and molecular analyses in children with benign epilepsy with centrotemporal spikes

BACKGROUND

Benign epilepsy with centrotemporal spikes (BECTS) is a common childhood epilepsy syndrome, accompanied by behavioral problems and cognitive impairments. Previous studies of BECTS-related brain structures applied univariate analysis and showed inconsistent results. And neurotransmitter patterns associated with brain structural alterations were still unclear.

METHODS

Structural images of twenty-one drug-naïve children with BECTS and thirty-five healthy controls (HCs) were scanned. Segmented gray matter volume (GMV) images were decomposed into independent components (ICs) using the source-based morphometry method. Then spatial correlation analyses were applied to examine possible relationships between GMV changes and neurotransmitter systems.

RESULTS

Compared with HCs, drug-naïve children with BECTS showed increased volume in one GMV component (IC7), including bilateral precentral gyrus, bilateral supplementary motor area, left superior frontal cortex, bilateral middle/ inferior frontal cortex and bilateral anterior/ middle cingulate cortex. A positive correlation was observed between one GMV component (IC6) and seizure frequency. There were significantly positive correlations between abnormal GMV in IC7 and serotonergic, GABAergic and glutamatergic systems.

CONCLUSION

These findings provided further evidence of changed GMV in drug-naïve children with BECTS related to their behavioral problems and cognitive impairments, and associated neurotransmitters which could help to better understand neurobiological mechanisms and underlying molecular mechanisms of BECTS.

IMPACT

The article provides further evidence of changed gray matter volume in drug-naïve children with BECTS related to their behavioral problems and cognitive impairments as well as associated neurotransmitters. Most literature to date has applied univariate analysis and showed inconsistent results, and neurotransmitter patterns associated with brain structural alterations were still unclear. Therefore, this article uses multivariate method and JuSpace toolbox to fill the gap. Significantly increased gray matter volume was found in drug-naïve children with BECTS compared with healthy controls. Abnormal gray matter volume was significantly correlated with clinical data and specific neurotransmitters.

Deciphering brain cellular and behavioral mechanisms: Insights from single-cell and spatial RNA sequencing

The brain is a complex computing system composed of a multitude of interacting neurons. The computational outputs of this system determine the behavior and perception of every individual. Each brain cell expresses thousands of genes that dictate the cell's function and physiological properties. Therefore, deciphering the molecular expression of each cell is of great significance for understanding its characteristics and role in brain function. Additionally, the positional information of each cell can provide crucial insights into their involvement in local brain circuits. In this review, we briefly overview the principles of single-cell RNA sequencing and spatial transcriptomics, the potential issues and challenges in their data processing, and their applications in brain research. We further outline several promising directions in neuroscience that could be integrated with single-cell RNA sequencing, including neurodevelopment, the identification of novel brain microstructures, cognition and behavior, neuronal cell positioning, molecules and cells related to advanced brain functions, sleep-wake cycles/circadian rhythms, and computational modeling of brain function. We believe that the deep integration of these directions with single-cell and spatial RNA sequencing can contribute significantly to understanding the roles of individual cells or cell types in these specific functions, thereby making important contributions to addressing critical questions in those fields. This article is categorized under: RNA Evolution and Genomics > Computational Analyses of RNA RNA in Disease and Development > RNA in Development RNA in Disease and Development > RNA in Disease.

Neurogenic and angiogenic poly(N-acryloylglycine)-co-(acrylamide)-co-(N-acryloyl-glutamate) hydrogel: preconditioning effect under oxidative stress and use in neuroregeneration

Traumatic injuries, neurodegenerative diseases and oxidative stress serve as the early biomarkers for neuronal damage and impede angiogenesis and subsequently neuronal growth. Considering this, the present work aimed to develop a poly(N-acryloylglycine)-co-(acrylamide)-co-(N-acryloylglutamate) hydrogel [p(NAG-Ac-NAE)] with angiogenesis/neurogenesis properties. As constituents of this polymer modulate their vital role in biological functions, inhibitory neurotransmitter glycine regulates neuronal homeostasis, and glutamatergic signalling regulates angiogenesis. The p(NAG-Ac-NAE) hydrogel is a highly branched, biodegradable and pH-responsive polymer with a very high swelling behavior of 6188%. The mechanical stability (G', 2.3-2.7 kPa) of this polymeric hydrogel is commendable in the differentiation of mature neurons. This hydrogel is biocompatible (as tested in HUVEC cells) and helps to proliferate PC12 cells (152.7 ± 13.7%), whereas it is cytotoxic towards aggressive cancers such as glioblastoma (LN229 cells) and triple negative breast cancer (TNBC; MDA-MB-231 cells) and helps to maintain the healthy cytoskeleton framework structure of primary cortical neurons by facilitating the elongation of the axonal pathway. Furthermore, FACS results revealed that the synthesized hydrogel potentiates neurogenesis by inducing the cell cycle (G0/G1) and arresting the sub-G1 phase by limiting apoptosis. Additionally, RT-PCR results revealed that this hydrogel induced an increased level of HIF-1α expression, providing preconditioning effects towards neuronal cells under oxidative stress by scavenging ROS and initiating neurogenic and angiogenic signalling. This hydrogel further exhibits more pro-angiogenic activities by increasing the expression of VEGF isoforms compared to previously reported hydrogels. In conclusion, the newly synthesized p(NAG-Ac-NAE) hydrogel can be one of the potential neuroregenerative materials for vasculogenesis-assisted neurogenic applications and paramount for the management of neurodegenerative diseases.

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated With Psychopathology Across Independent Cohorts

BACKGROUND

Patients with early psychosis (EP) (within 3 years after psychosis onset) show significant variability, which makes predicting outcomes challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, which limits the development of early interventions.

METHODS

A data-driven approach, partial least squares correlation, was used across 2 independent datasets to examine multivariate relationships between white matter properties and symptomatology and to identify stable and generalizable signatures in EP. The primary cohort included patients with EP from the Human Connectome Project for Early Psychosis (n = 124). The replication cohort included patients with EP from the Feinstein Institute for Medical Research (n = 78) as part of the MEND (Multimodal Evaluation of Neural Disorders) Project. Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

RESULTS

In both cohorts, a significant latent component corresponded to a symptom profile that combined negative symptoms, primarily diminished expression, with specific somatic symptoms. Both latent components captured comprehensive features of white matter disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the partial least squares model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

CONCLUSIONS

This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural white matter alterations in EP across diagnoses and datasets, showing strong covariance of these alterations with a unique profile of negative and somatic symptoms. These findings suggest the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

The Case for Neurosurgical Intervention in Cancer Neuroscience

The emerging field of cancer neuroscience reshapes our understanding of the intricate relationship between the nervous system and cancer biology; this new paradigm is likely to fundamentally change and advance neuro-oncological care. The profound interplay between cancers and the nervous system is reciprocal: Cancer growth can be induced and regulated by the nervous system; conversely, tumors can themselves alter the nervous system. Such crosstalk between cancer cells and the nervous system is evident in both the peripheral and central nervous systems. Recent advances have uncovered numerous direct neuron-cancer interactions at glioma-neuronal synapses, paracrine mechanisms within the tumor microenvironment, and indirect neuroimmune interactions. Neurosurgeons have historically played a central role in neuro-oncological care, and as the field of cancer neuroscience is becoming increasingly established, the role of neurosurgical intervention is becoming clearer. Examples include peripheral denervation procedures, delineation of neuron-glioma networks, development of neuroprostheses, neuromodulatory procedures, and advanced local delivery systems. The present review seeks to highlight key cancer neuroscience mechanisms with neurosurgical implications and outline the future role of neurosurgical intervention in cancer neuroscience.

Adolescent maturation of cortical excitation-inhibition balance based on individualized biophysical network modeling

The balance of excitation and inhibition is a key functional property of cortical microcircuits which changes through the lifespan. Adolescence is considered a crucial period for the maturation of excitation-inhibition balance. This has been primarily observed in animal studies, yet human in vivo evidence on adolescent maturation of the excitation-inhibition balance at the individual level is limited. Here, we developed an individualized in vivo marker of regional excitation-inhibition balance in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional magnetic resonance imaging data from two independent cross-sectional (N = 752) and longitudinal (N = 149) cohorts. We found a widespread relative increase of inhibition in association cortices paralleled by a relative age-related increase of excitation, or lack of change, in sensorimotor areas across both datasets. This developmental pattern co-aligned with multiscale markers of sensorimotor-association differentiation. The spatial pattern of excitation-inhibition development in adolescence was robust to inter-individual variability of structural connectomes and modeling configurations. Notably, we found that alternative simulation-based markers of excitation-inhibition balance show a variable sensitivity to maturational change. Taken together, our study highlights an increase of inhibition during adolescence in association areas using cross sectional and longitudinal data, and provides a robust computational framework to estimate microcircuit maturation in vivo at the individual level.

Correlation between cerebral neurotransmitters levels by proton magnetic resonance spectroscopy and HbA1c in patients with type 2 diabetes

BACKGROUND

Cognitive dysfunction is the main manifestation of central neuropathy. Although cognitive impairments tend to be overlooked in patients with diabetes mellitus (DM), there is a growing body of evidence linking DM to cognitive dysfunction. Hyperglycemia is closely related to neurological abnormalities, while often disregarded in clinical practice. Changes in cerebral neurotransmitter levels are associated with a variety of neurological abnormalities and may be closely related to blood glucose control in patients with type 2 DM (T2DM).

AIM

To evaluate the concentrations of cerebral neurotransmitters in T2DM patients exhibiting different hemoglobin A1c (HbA1c) levels.

METHODS

A total of 130 T2DM patients were enrolled at the Department of Endocrinology of Shanghai East Hospital. The participants were divided into four groups according to their HbA1c levels using the interquartile method, namely Q1 (< 7.875%), Q2 (7.875%-9.050%), Q3 (9.050%-11.200%) and Q4 (≥ 11.200%). Clinical data were collected and measured, including age, height, weight, neck/waist/hip circumferences, blood pressure, comorbidities, duration of DM, and biochemical indicators. Meanwhile, neurotransmitters in the left hippocampus and left brainstem area were detected by proton magnetic resonance spectroscopy.

RESULTS

The HbA1c level was significantly associated with urinary microalbumin (mALB), triglyceride, low-density lipoprotein cholesterol (LDL-C), homeostasis model assessment of insulin resistance (HOMA-IR), and beta cell function (HOMA-β), N-acetylaspartate/creatine (NAA/Cr), and NAA/choline (NAA/Cho). Spearman correlation analysis showed that mALB, LDL-C, HOMA-IR and NAA/Cr in the left brainstem area were positively correlated with the level of HbA1c (P < 0.05), whereas HOMA-β was negatively correlated with the HbA1c level (P < 0.05). Ordered multiple logistic regression analysis showed that NAA/Cho [Odds ratio (OR): 1.608, 95% confidence interval (95%CI): 1.004-2.578, P < 0.05], LDL-C (OR: 1.627, 95%CI: 1.119-2.370, P < 0.05), and HOMA-IR (OR: 1.107, 95%CI: 1.031-1.188, P < 0.01) were independent predictors of poor glycemic control.

CONCLUSION

The cerebral neurotransmitter concentrations in the left brainstem area in patients with T2DM are closely related to glycemic control, which may be the basis for the changes in cognitive function in diabetic patients.

Neuroimaging epicenters as potential sites of onset of the neuroanatomical pathology in schizophrenia

Schizophrenia lacks a clear definition at the neuroanatomical level, capturing the sites of origin and progress of this disorder. Using a network-theory approach called epicenter mapping on cross-sectional magnetic resonance imaging from 1124 individuals with schizophrenia, we identified the most likely "source of origin" of the structural pathology. Our results suggest that the Broca's area and adjacent frontoinsular cortex may be the epicenters of neuroanatomical pathophysiology in schizophrenia. These epicenters can predict an individual's response to treatment for psychosis. In addition, cross-diagnostic similarities based on epicenter mapping over of 4000 individuals diagnosed with neurological, neurodevelopmental, or psychiatric disorders appear to be limited. When present, these similarities are restricted to bipolar disorder, major depressive disorder, and obsessive-compulsive disorder. We provide a comprehensive framework linking schizophrenia-specific epicenters to multiple levels of neurobiology, including cognitive processes, neurotransmitter receptors and transporters, and human brain gene expression. Epicenter mapping may be a reliable tool for identifying the potential onset sites of neural pathophysiology in schizophrenia.

Early Cortical Microstructural Changes in Aging Are Linked to Vulnerability to Alzheimer's Disease Pathology

BACKGROUND

Early identification of Alzheimer's disease (AD) risk is critical for improving treatment success. Cortical thickness is a macrostructural measure used to assess neurodegeneration in AD. However, cortical microstructural changes appear to precede macrostructural atrophy and may improve early risk identification. Currently, whether cortical microstructural changes in aging are linked to vulnerability to AD pathophysiology remains unclear in nonclinical populations, who are precisely the target for early risk identification.

METHODS

In 194 adults, we calculated magnetic resonance imaging-derived maps of changes in cortical mean diffusivity (microstructure) and cortical thickness (macrostructure) over 5 to 6 years (mean age: time 1 = 61.82 years; time 2 = 67.48 years). Episodic memory was assessed using 3 well-established tests. We obtained positron emission tomography-derived maps of AD pathology deposition (amyloid-β, tau) and neurotransmitter receptors (cholinergic, glutamatergic) implicated in AD pathophysiology. Spatial correlational analyses were used to compare pattern similarity among maps.

RESULTS

Spatial patterns of cortical macrostructural changes resembled patterns of cortical organization sensitive to age-related processes (r = -0.31, p < .05), whereas microstructural changes resembled the patterns of tau deposition in AD (r = 0.39, p = .038). Individuals with patterns of microstructural changes that more closely resembled stereotypical tau deposition exhibited greater memory decline (β = 0.22, p = .029). Microstructural changes and AD pathology deposition were enriched in areas with greater densities of cholinergic and glutamatergic receptors (ps < .05).

CONCLUSIONS

Patterns of cortical microstructural changes were more AD-like than patterns of macrostructural changes, which appeared to reflect more general aging processes. Microstructural changes may better inform early risk prediction efforts as a sensitive measure of vulnerability to pathological processes prior to overt atrophy and cognitive decline.

Disrupted topological properties of structural brain networks present a glutamatergic neuropathophysiology in people with narcolepsy

STUDY OBJECTIVES

Growing evidences have documented various abnormalities of the white matter bundles in people with narcolepsy. We sought to evaluate topological properties of brain structural networks, and their association with symptoms and neuropathophysiological features in people with narcolepsy.

METHODS

Diffusion tensor imaging was conducted for people with narcolepsy (n = 30) and matched healthy controls as well as symptoms assessment. Structural connectivity for each participant was generated to analyze global and regional topological properties and their correlations with narcoleptic features. Further human brain transcriptome was extracted and spatially registered for connectivity vulnerability. Genetic functional enrichment analysis was performed and further clarified using in vivo emission computed tomography data.

RESULTS

A wide and dramatic decrease in structural connectivities was observed in people with narcolepsy, with descending network degree and global efficiency. These metrics were not only correlated with sleep latency and awakening features, but also reflected alterations of sleep macrostructure in people with narcolepsy. Network-based statistics identified a small hyperenhanced subnetwork of cingulate gyrus that was closely related to rapid eye movement sleep behavior disorder (RBD) in narcolepsy. Further imaging genetics analysis suggested glutamatergic signatures were responsible for the preferential vulnerability of connectivity alterations in people with narcolepsy, while additional PET/SPECT data verified that structural alteration was significantly correlated with metabotropic glutamate receptor 5 (mGlutR5) and N-methyl-D-aspartate receptor (NMDA).

CONCLUSIONS

People with narcolepsy endured a remarkable decrease in the structural architecture, which was not only closely related to narcolepsy symptoms but also glutamatergic signatures.

Tau follows principal axes of functional and structural brain organization in Alzheimer's disease

Alzheimer's disease (AD) is a brain network disorder where pathological proteins accumulate through networks and drive cognitive decline. Yet, the role of network connectivity in facilitating this accumulation remains unclear. Using in-vivo multimodal imaging, we show that the distribution of tau and reactive microglia in humans follows spatial patterns of connectivity variation, the so-called gradients of brain organization. Notably, less distinct connectivity patterns ("gradient contraction") are associated with cognitive decline in regions with greater tau, suggesting an interaction between reduced network differentiation and tau on cognition. Furthermore, by modeling tau in subject-specific gradient space, we demonstrate that tau accumulation in the frontoparietal and temporo-occipital cortices is associated with greater baseline tau within their functionally and structurally connected hubs, respectively. Our work unveils a role for both functional and structural brain organization in pathology accumulation in AD, and supports subject-specific gradient space as a promising tool to map disease progression.

PET-MRI Applications and Future Prospects in Psychiatry

This article reviews the synergistic application of positron emission tomography-magnetic resonance imaging (PET-MRI) in neuroscience with relevance for psychiatry, particularly examining neurotransmission, epigenetics, and dynamic imaging methodologies. We begin by discussing the complementary insights that PET and MRI modalities provide into neuroreceptor systems, with a focus on dopamine, opioids, and serotonin receptors, and their implications for understanding and treating psychiatric disorders. We further highlight recent PET-MRI studies using a radioligand that enables the quantification of epigenetic enzymes, specifically histone deacetylases, in the brain in vivo. Imaging epigenetics is used to exemplify the impact the quantification of novel molecular targets may have, including new treatment approaches for psychiatric disorders. Finally, we discuss innovative designs involving functional PET using [18F]FDG (fPET-FDG), which provides detailed information regarding dynamic changes in glucose metabolism. Concurrent acquisitions of fPET-FDG and functional MRI provide a time-resolved approach to studying brain function, yielding simultaneous metabolic and hemodynamic information and thereby opening new avenues for psychiatric research. Collectively, the review underscores the potential of a multimodal PET-MRI approach to advance our understanding of brain structure and function in health and disease, which could improve clinical care based on objective neurobiological features and treatment response monitoring. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

Normative modelling of molecular-based functional circuits captures clinical heterogeneity transdiagnostically in psychiatric patients

Advanced methods such as REACT have allowed the integration of fMRI with the brain's receptor landscape, providing novel insights transcending the multiscale organisation of the brain. Similarly, normative modelling has allowed translational neuroscience to move beyond group-average differences and characterise deviations from health at an individual level. Here, we bring these methods together for the first time. We used REACT to create functional networks enriched with the main modulatory, inhibitory, and excitatory neurotransmitter systems and generated normative models of these networks to capture functional connectivity deviations in patients with schizophrenia, bipolar disorder (BPD), and ADHD. Substantial overlap was seen in symptomatology and deviations from normality across groups, but these could be mapped into a common space linking constellations of symptoms through to underlying neurobiology transdiagnostically. This work provides impetus for developing novel biomarkers that characterise molecular- and systems-level dysfunction at the individual level, facilitating the transition towards mechanistically targeted treatments.

Systematic evaluation of fMRI data-processing pipelines for consistent functional connectomics

Functional interactions between brain regions can be viewed as a network, enabling neuroscientists to investigate brain function through network science. Here, we systematically evaluate 768 data-processing pipelines for network reconstruction from resting-state functional MRI, evaluating the effect of brain parcellation, connectivity definition, and global signal regression. Our criteria seek pipelines that minimise motion confounds and spurious test-retest discrepancies of network topology, while being sensitive to both inter-subject differences and experimental effects of interest. We reveal vast and systematic variability across pipelines' suitability for functional connectomics. Inappropriate choice of data-processing pipeline can produce results that are not only misleading, but systematically so, with the majority of pipelines failing at least one criterion. However, a set of optimal pipelines consistently satisfy all criteria across different datasets, spanning minutes, weeks, and months. We provide a full breakdown of each pipeline's performance across criteria and datasets, to inform future best practices in functional connectomics.

Morphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance

The human brain is organized as segregation and integration units and follows complex developmental trajectories throughout life. The cortical manifold provides a new means of studying the brain's organization in a multidimensional connectivity gradient space. However, how the brain's morphometric organization changes across the human lifespan remains unclear. Here, leveraging structural magnetic resonance imaging scans from 1,790 healthy individuals aged 8 to 89 years, we investigated age-related global, within- and between-network dispersions to reveal the segregation and integration of brain networks from 3D manifolds based on morphometric similarity network (MSN), combining multiple features conceptualized as a "fingerprint" of an individual's brain. Developmental trajectories of global dispersion unfolded along patterns of molecular brain organization, such as acetylcholine receptor. Communities were increasingly dispersed with age, reflecting more disassortative morphometric similarity profiles within a community. Increasing within-network dispersion of primary motor and association cortices mediated the influence of age on the cognitive flexibility of executive functions. We also found that the secondary sensory cortices were decreasingly dispersed with the rest of the cortices during aging, possibly indicating a shift of secondary sensory cortices across the human lifespan from an extreme to a more central position in 3D manifolds. Together, our results reveal the age-related segregation and integration of MSN from the perspective of a multidimensional gradient space, providing new insights into lifespan changes in multiple morphometric features of the brain, as well as the influence of such changes on cognitive performance.

Unraveling the molecular relevance of brain phenotypes: A comparative analysis of null models and test statistics

Correlating transcriptional profiles with imaging-derived phenotypes has the potential to reveal possible molecular architectures associated with cognitive functions, brain development and disorders. Competitive null models built by resampling genes and self-contained null models built by spinning brain regions, along with varying test statistics, have been used to determine the significance of transcriptional associations. However, there has been no systematic evaluation of their performance in imaging transcriptomics analyses. Here, we evaluated the performance of eight different test statistics (mean, mean absolute value, mean squared value, max mean, median, Kolmogorov-Smirnov (KS), Weighted KS and the number of significant correlations) in both competitive null models and self-contained null models. Simulated brain maps (n = 1,000) and gene sets (n = 500) were used to calculate the probability of significance (Psig) for each statistical test. Our results suggested that competitive null models may result in false positive results driven by co-expression within gene sets. Furthermore, we demonstrated that the self-contained null models may fail to account for distribution characteristics (e.g., bimodality) of correlations between all available genes and brain phenotypes, leading to false positives. These two confounding factors interacted differently with test statistics, resulting in varying outcomes. Specifically, the sign-sensitive test statistics (i.e., mean, median, KS, Weighted KS) were influenced by co-expression bias in the competitive null models, while median and sign-insensitive test statistics were sensitive to the bimodality bias in the self-contained null models. Additionally, KS-based statistics produced conservative results in the self-contained null models, which increased the risk of false negatives. Comprehensive supplementary analyses with various configurations, including realistic scenarios, supported the results. These findings suggest utilizing sign-insensitive test statistics such as mean absolute value, max mean in the competitive null models and the mean as the test statistic for the self-contained null models. Additionally, adopting the confounder-matched (e.g., coexpression-matched) null models as an alternative to standard null models can be a viable strategy. Overall, the present study offers insights into the selection of statistical tests for imaging transcriptomics studies, highlighting areas for further investigation and refinement in the evaluation of novel and commonly used tests.

Neural correlates of obesity across the lifespan

Associations between brain and obesity are bidirectional: changes in brain structure and function underpin over-eating, while chronic adiposity leads to brain atrophy. Investigating brain-obesity interactions across the lifespan can help better understand these relationships. This study explores the interaction between obesity and cortical morphometry in children, young adults, adults, and older adults. We also investigate the genetic, neurochemical, and cognitive correlates of the brain-obesity associations. Our findings reveal a pattern of lower cortical thickness in fronto-temporal brain regions associated with obesity across all age cohorts and varying age-dependent patterns in the remaining brain regions. In adults and older adults, obesity correlates with neurochemical changes and expression of inflammatory and mitochondrial genes. In children and older adults, adiposity is associated with modifications in brain regions involved in emotional and attentional processes. Thus, obesity might originate from cognitive changes during early adolescence, leading to neurodegeneration in later life through mitochondrial and inflammatory mechanisms.

Temporal dissociation between local and global functional adaptations of the maternal brain to childbirth: a longitudinal assessment

The maternal brain undergoes significant reorganization during birth and the postpartum period. However, the temporal dynamics of these changes remain unclear. Using resting-state functional magnetic resonance imaging, we report on local and global brain function alterations in 75 mothers in their first postpartum week, compared to 23 nulliparous women. In a subsample followed longitudinally for the next six months, we observed a temporal and spatial dissociation between changes observed at baseline (cluster mass permutation: pFWE < 0.05). Local activity and connectivity changes in widespread neocortical regions persisted throughout the studied time period (ANCOVAs vs. controls: pFDR < 0.05), with preliminary evidence linking these alterations to behavioral and psychological adaptations (interaction effect with postpartum time: uncorrected p < 0.05). In contrast, the initially reduced whole-brain connectivity of putamen-centered subcortical areas returned to control levels within six to nine weeks postpartum (linear and quadratic mixed linear models: pFDR < 0.05). The whole-brain spatial colocalization with hormone receptor distributions (Spearman correlations: pFDR < 0.05) and preliminary blood hormone associations (interaction effect with postpartum time: uncorrected p < 0.05) suggested that the postpartum restoration of progesterone levels may underlie this rapid normalization. These observations enhance our understanding of healthy maternal brain function, contributing to the identification of potential markers for pathological postpartum adaptation processes, which in turn could underlie postpartum psychiatric disorders.

Decoding Early Psychoses: Unraveling Stable Microstructural Features Associated with Psychopathology Across Independent Cohorts

Background

Early Psychosis patients (EP, within 3 years after psychosis onset) show significant variability, making outcome predictions challenging. Currently, little evidence exists for stable relationships between neural microstructural properties and symptom profiles across EP diagnoses, limiting the development of early interventions.

Methods

A data-driven approach, Partial Least Squares (PLS) correlation, was used across two independent datasets to examine multivariate relationships between white matter (WM) properties and symptomatology, to identify stable and generalizable signatures in EP. The primary cohort included EP patients from the Human Connectome Project-Early Psychosis (n=124). The replication cohort included EP patients from the Feinstein Institute for Medical Research (n=78). Both samples included individuals with schizophrenia, schizoaffective disorder, and psychotic mood disorders.

Results

In both cohorts, a significant latent component (LC) corresponded to a symptom profile combining negative symptoms, primarily diminished expression, with specific somatic symptoms. Both LCs captured comprehensive features of WM disruption, primarily a combination of subcortical and frontal association fibers. Strikingly, the PLS model trained on the primary cohort accurately predicted microstructural features and symptoms in the replication cohort. Findings were not driven by diagnosis, medication, or substance use.

Conclusions

This data-driven transdiagnostic approach revealed a stable and replicable neurobiological signature of microstructural WM alterations in EP, across diagnoses and datasets, showing a strong covariance of these alterations with a unique profile of negative and somatic symptoms. This finding suggests the clinical utility of applying data-driven approaches to reveal symptom domains that share neurobiological underpinnings.

Altered intrinsic neural timescales and neurotransmitter activity in males with tobacco use disorder

Previous researches of tobacco use disorder (TUD) has overlooked the hierarchy of cortical functions and single modality design separated the relationship between macroscopic neuroimaging aberrance and microscopic molecular basis. At present, intrinsic timescale gradient of TUD and its molecular features are not fully understood. Our study recruited 146 male subjects, including 44 heavy smokers, 50 light smokers and 52 non-smokers, then obtained their rs-fMRI data and clinical scales related to smoking. Intrinsic neural timescale (INT) method was performed to describe how long neural information was stored in a brain region by calculating the autocorrelation function (ACF) of each voxel to examine the difference in the ability of information integration among the three groups. Then, correlation analyses were conducted to explore the relationship between INT abnormalities and clinical scales of smokers. Finally, cross-modal JuSpace toolbox was used to investigate the association between INT aberrance and the expression of specific receptor/transporters. Compared to healthy controls, TUD subjects displayed decreased INT in control network (CN), default mode network (DMN), sensorimotor areas and visual cortex, and such trend of decreasing INT was more pronounced in heavy smokers. Moreover, various neurotransmitters (including dopaminergic, acetylcholine and μ-opioid receptors) were involved in the molecular mechanism of timescale decreasing and differed in heavy and light smokers. These findings supplied novel insights into the brain functional aberrance in TUD from an intrinsic neural dynamic perspective and confirm INT was a potential neurobiological marker. And also established the connection between macroscopic imaging aberrance and microscopic molecular changes in TUD.

Diverging functional connectivity timescales: Capturing distinct aspects of cognitive performance in early psychosis

Background

Psychosis spectrum disorders (PSDs) are marked by cognitive impairments, the neurobiological correlates of which remain poorly understood. Here, we investigate the entropy of time-varying functional connectivity (TVFC) patterns from resting-state fMRI (rfMRI) as potential biomarker for cognitive performance in PSDs. By combining our results with multimodal reference data, we hope to generate new insights into the mechanisms underlying cognitive dysfunction in PSDs. We hypothesized that low-entropy TVFC patterns (LEN) would be more behaviorally informative than high-entropy TVFC patterns (HEN), especially for tasks that require extensive integration across diverse cognitive subdomains.

Methods

rfMRI and behavioral data from 97 patients in the early phases of psychosis and 53 controls were analyzed. Positron-Emission Tomography (PET) and magnetoencephalography (MEG) data were taken from a public repository (Hansen et al., 2022). Multivariate analyses were conducted to examine relationships between TVFC patterns at multiple spatial scales and cognitive performance in patients.

Results

Compared to HEN, LEN explained significantly more cognitive variance on average in PSD patients, driven by superior encoding of information on psychometrically more integrated tasks. HEN better captured information in specific subdomains of executive functioning. Nodal HEN-LEN transitions were spatially aligned with neurobiological gradients reflecting monoaminergic transporter densities and MEG beta power. Exploratory analyses revealed a close statistical relationship between LEN and positive PSD symptoms.

Conclusion

Our entropy-based analysis of TVFC patterns dissociates distinct aspects of cognition in PSDs. By linking topographies of neurotransmission and oscillatory dynamics with cognitive performance, it enhances our understanding of the mechanisms underlying cognitive deficits in PSDs.

CRediT Authorship Contribution Statement

Fabian Hirsch: Conceptualization, Methodology, Software, Formal analysis, Writing - Original Draft, Writing - Review & Editing, Visualization; Ângelo Bumanglag: Methodology, Software, Formal analysis, Writing - Review & Editing; Yifei Zhang: Methodology, Software, Formal analysis, Writing - Review & Editing; Afra Wohlschlaeger: Methodology, Writing - Review & Editing, Supervision, Project administration.

Regional patterns of human cortex development colocalize with underlying neurobiology

Human brain morphology undergoes complex changes over the lifespan. Despite recent progress in tracking brain development via normative models, current knowledge of underlying biological mechanisms is highly limited. We demonstrate that human cerebral cortex development and aging trajectories unfold along patterns of molecular and cellular brain organization, traceable from population-level to individual developmental trajectories. During childhood and adolescence, cortex-wide spatial distributions of dopaminergic receptors, inhibitory neurons, glial cell populations, and brain-metabolic features explain up to 50% of variance associated with a lifespan model of regional cortical thickness trajectories. In contrast, modeled cortical change patterns during adulthood are best explained by cholinergic and glutamatergic neurotransmitter receptor and transporter distributions. These relationships are supported by developmental gene expression trajectories and translate to individual longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at cohort- and 18% at single-subject level. Integrating neurobiological brain atlases with normative modeling and population neuroimaging provides a biologically meaningful path to understand brain development and aging in living humans.

The meso-connectomes of mouse, marmoset, and macaque: network organization and the emergence of higher cognition

The recent publications of the inter-areal connectomes for mouse, marmoset, and macaque cortex have allowed deeper comparisons across rodent vs. primate cortical organization. In general, these show that the mouse has very widespread, "all-to-all" inter-areal connectivity (i.e. a "highly dense" connectome in a graph theoretical framework), while primates have a more modular organization. In this review, we highlight the relevance of these differences to function, including the example of primary visual cortex (V1) which, in the mouse, is interconnected with all other areas, therefore including other primary sensory and frontal areas. We argue that this dense inter-areal connectivity benefits multimodal associations, at the cost of reduced functional segregation. Conversely, primates have expanded cortices with a modular connectivity structure, where V1 is almost exclusively interconnected with other visual cortices, themselves organized in relatively segregated streams, and hierarchically higher cortical areas such as prefrontal cortex provide top-down regulation for specifying precise information for working memory storage and manipulation. Increased complexity in cytoarchitecture, connectivity, dendritic spine density, and receptor expression additionally reveal a sharper hierarchical organization in primate cortex. Together, we argue that these primate specializations permit separable deconstruction and selective reconstruction of representations, which is essential to higher cognition.

Brain connectivity changes to fast versus slow dopamine increases

The rewarding effects of stimulant drugs such as methylphenidate (MP) depend crucially on how fast they raise dopamine in the brain. Yet how the rate of drug-induced dopamine increases impacts brain network communication remains unresolved. We manipulated route of MP administration to generate fast versus slow dopamine increases. We hypothesized that fast versus slow dopamine increases would result in a differential pattern of global brain connectivity (GBC) in association with regional levels of dopamine D1 receptors, which are critical for drug reward. Twenty healthy adults received MP intravenously (0.5 mg/kg; fast dopamine increases) and orally (60 mg; slow dopamine increases) during simultaneous [11C]raclopride PET-fMRI scans (double-blind, placebo-controlled). We tested how GBC was temporally associated with slow and fast dopamine increases on a minute-to-minute basis. Connectivity patterns were strikingly different for slow versus fast dopamine increases, and whole-brain spatial patterns were negatively correlated with one another (rho = -0.54, pspin < 0.001). GBC showed "fast>slow" associations in dorsal prefrontal cortex, insula, posterior thalamus and brainstem, caudate and precuneus; and "slow>fast" associations in ventral striatum, orbitofrontal cortex, and frontopolar cortex (pFDR < 0.05). "Fast>slow" GBC patterns showed significant spatial correspondence with D1 receptor availability (estimated via normative maps of [11C]SCH23390 binding; rho = 0.22, pspin < 0.05). Further, hippocampal GBC to fast dopamine increases was significantly negatively correlated with self-reported 'high' ratings to intravenous MP across individuals (r(19) = -0.68, pbonferroni = 0.015). Different routes of MP administration produce divergent patterns of brain connectivity. Fast dopamine increases are uniquely associated with connectivity patterns that have relevance for the subjective experience of drug reward.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures

AIMS

This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data.

METHODS

The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding.

RESULTS

Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05).

CONCLUSIONS

Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Computational limits to the legibility of the imaged human brain

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

A systems identification approach using Bayes factors to deconstruct the brain bases of emotion regulation

Cognitive reappraisal is fundamental to cognitive therapies and everyday emotion regulation. Analyses using Bayes factors and an axiomatic systems identification approach identified four reappraisal-related components encompassing distributed neural activity patterns across two independent functional magnetic resonance imaging (fMRI) studies (n = 182 and n = 176): (1) an anterior prefrontal system selectively involved in cognitive reappraisal; (2) a fronto-parietal-insular system engaged by both reappraisal and emotion generation, demonstrating a general role in appraisal; (3) a largely subcortical system activated during negative emotion generation but unaffected by reappraisal, including amygdala, hypothalamus and periaqueductal gray; and (4) a posterior cortical system of negative emotion-related regions downregulated by reappraisal. These systems covaried with individual differences in reappraisal success and were differentially related to neurotransmitter binding maps, implicating cannabinoid and serotonin systems in reappraisal. These findings challenge 'limbic'-centric models of reappraisal and provide new systems-level targets for assessing and enhancing emotion regulation.