neuromaps: structural and functional interpretation of brain maps

The spatial layout of antagonistic brain regions is explicable based on geometric principles

Brain activity emerges in a dynamic landscape of regional increases and decreases that span the cortex. Increases in activity during a cognitive task are often assumed to reflect the processing of task-relevant information, while reductions can be interpreted as suppression of irrelevant activity to facilitate task goals. Here, we explore the relationship between task-induced increases and decreases in activity from a geometric perspective. Using a technique known as kriging, developed in earth sciences, we examined whether the spatial organisation of brain regions showing positive activity could be predicted based on the spatial layout of regions showing activity decreases (and vice versa). Consistent with this hypothesis we established the spatial distribution of regions showing reductions in activity could predict (i) regions showing task-relevant increases in activity in both groups of humans and single individuals; (ii) patterns of neural activity captured by calcium imaging in mice; and, (iii) showed a high degree of generalisability across task contexts. Our analysis, therefore, establishes that antagonistic relationships between brain regions are topographically determined, a spatial analog for the well documented anti-correlation between brain systems over time.

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

The excitation-inhibition ratio is a key functional property of cortical microcircuits which changes throughout an individual's lifespan. Adolescence is considered a critical period for maturation of excitation-inhibition ratio. This has primarily been observed in animal studies. However, there is limited human in vivo evidence for maturation of excitation-inhibition ratio at the individual level. Here, we developed an individualized in vivo marker of regional excitation-inhibition ratio in human adolescents, estimated using large-scale simulations of biophysical network models fitted to resting-state functional imaging data from both cross-sectional (n = 752) and longitudinal (n = 149) cohorts. In both datasets, we found a widespread decrease in excitation-inhibition ratio in association areas, paralleled by an increase or lack of change in sensorimotor areas. This developmental pattern was aligned with multiscale markers of sensorimotor-association differentiation. Although our main findings were robust across alternative modeling configurations, we observed local variations, highlighting the importance of methodological choices for future studies.

Intrinsic dynamic shapes responses to external stimulation in the human brain

Sensory stimulation of the brain reverberates in its recurrent neural networks. However, current computational models of brain activity do not separate immediate sensory responses from this intrinsic dynamic. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans. This model captures the extrinsic effect of the stimulus and separates that from the intrinsic effect of the recurrent brain dynamic. We find that the intrinsic dynamic enhances and prolongs the neural responses to scene cuts, eye movements, and sounds. Failing to account for these extrinsic inputs, leads to spurious recurrent connections that govern the intrinsic dynamic. We also find that the recurrent connectivity during rest is reduced during movie watching. The model shows that an external stimulus can reduce intrinsic noise. It also shows that sensory areas have mostly outward, whereas higher-order brain areas mostly incoming connections. We conclude that the response to an external audiovisual stimulus can largely be attributed to the intrinsic dynamic of the brain, already observed during rest.

Coupled Aging of Cyto- and Myeloarchitectonic Atlas-Informed Gray and White Matter Structural Properties

A key aspect of brain aging that remains poorly understood is its high regional heterogeneity and heterochronicity. A better understanding of how the structural organization of the brain shapes aging trajectories is needed. Neuroimaging tissue "types" are often collected and analyzed as separate acquisitions, an approach that cannot provide a holistic view of age-related change in the related portions of the neurons (cell bodies and axons). Because neuroimaging can only assess indirect features at the gross macrostructural level, incorporating post-mortem histological information may aid in a better understanding of structural aging gradients. Longitudinal design, coupling of gray and white matter (GM and WM) properties, and a biologically informed approach to organizing neural properties are needed. Thus, we tested aging of the regional coupling between GM (cortical thickness, surface area, volume) and WM (fractional anisotropy, mean, axial, and radial diffusivities) structural metrics using linear mixed effects modeling in 102 healthy adults aged 20-94 years old, scanned on two occasions over a four-year period. The association between age-related within-person change in GM morphometry and the diffusion properties of the directly neighboring portion of white matter was assessed, capturing both aspects of neuronal health in one model. Additionally, we parcellated the brain utilizing the histological-staining informed von Economo-Koskinas atlas to consider regional cyto- and myelo-architecture. Results demonstrate several gradients of coupled association in the age-related decline of neighboring white and gray matter. Most notably, gradients of coupling along the heteromodal association to sensory axis were found for several areas (e.g., anterior frontal and lateral temporal cortices, vs. pre- and post-central gyrus, occipital, and limbic areas), in line with heterochronicity and retrogenesis theories of aging. Further effort to bridge across data and measurement scales will enhance understanding of the mechanisms of the aging brain.

Autonomic physiological coupling of the global fMRI signal

The brain is closely attuned to visceral signals from the body's internal environment, as evidenced by the numerous associations between neural, hemodynamic and peripheral physiological signals. Here we show that a major mode of these brain-body cofluctuations can be captured by a single spatiotemporal pattern. Across several independent samples, as well as single-echo and multi-echo functional magnetic resonance imaging (fMRI) data acquisition sequences, we identify widespread cofluctuations in the low-frequency range (0.01-0.1 Hz) between resting-state global fMRI signals, electroencephalogram (EEG) activity, and a host of peripheral autonomic signals spanning cardiovascular, pulmonary, exocrine and smooth muscle systems. The same brain-body cofluctuations observed at rest are elicited by cued deep breathing and intermittent sensory stimuli, as well as spontaneous phasic EEG events during sleep. Furthermore, we show that the spatial structure of global fMRI signals is maintained under experimental suppression of end-tidal carbon dioxide variations, suggesting that respiratory-driven fluctuations in arterial CO2 accompanying arousal cannot fully explain the origin of these signals in the brain. These findings suggest that the global fMRI signal is a substantial component of the arousal response governed by the autonomic nervous system.

Convergent large-scale network and local vulnerabilities underlie brain atrophy across Parkinson's disease stages: a worldwide ENIGMA study

Parkinson's disease (PD) is associated with extensive structural brain changes. Recent work has proposed that the spatial pattern of disease pathology is shaped by both network spread and local vulnerability. However, only few studies assessed these biological frameworks in large patient samples across disease stages. Analyzing the largest imaging cohort in PD to date (N = 3,096 patients), we investigated the roles of network architecture and local brain features by relating regional abnormality maps to normative profiles of connectivity, intrinsic networks, cytoarchitectonics, neurotransmitter receptor densities, and gene expression. We found widespread cortical and subcortical atrophy in PD to be associated with advancing disease stage, longer time since diagnosis, and poorer global cognition. Structural brain connectivity best explained cortical atrophy patterns in PD and across disease stages. These patterns were robust among individual patients. The precuneus, lateral temporal cortex, and amygdala were identified as likely network-based epicentres, with high convergence across disease stages. Individual epicentres varied significantly among patients, yet they consistently localized to the default mode and limbic networks. Furthermore, we showed that regional overexpression of genes implicated in synaptic structure and signalling conferred increased susceptibility to brain atrophy in PD. In summary, this study demonstrates in a well-powered sample that structural brain abnormalities in PD across disease stages and within individual patients are influenced by both network spread and local vulnerability.

Association of Cortical Atrophy Patterns With Clinical Phenotypes and Histopathological Findings in Patients With Rasmussen Syndrome

BACKGROUND AND OBJECTIVES

Automated MRI analyses have identified variable patterns of cortical atrophy in Rasmussen syndrome. In this study, we aim to identify imaging phenotypes of Rasmussen syndrome, to clinically characterize these phenotypes, and to validate this imaging-based approach through histopathologic analysis.

METHODS

For this retrospective case-control study, individuals with Rasmussen syndrome diagnosed according to the European Consensus Statement and at least one 3D T1-weighted MRI scan (<20 years after onset) were identified from the University Hospital Bonn (1995-2023). Healthy controls were selected from databases at the University Hospital Bonn, Charité University Hospital Berlin, and the Human Connectome Project. Disease epicenters, describing brain regions highly connected to atrophy regions, were mapped individually using network-based atrophy modeling. Subtypes were identified through k-means clustering. Neuropsychological test results and results from neuropathologic analyses of biopsies were ascertained, and correlations between subtype-specific atrophy maps and normative maps (enhancing neuro imaging genetics through meta analysis [ENIGMA] and neuromaps toolbox) were used to characterize atrophy profiles and epicenter susceptibility.

RESULTS

The study incorporated 54 individuals with Rasmussen syndrome (median age at MRI: 18 years, range 2-61, 65% female) and 270 healthy individuals (median age at MRI: 26.5 years, range 3-61, 49% female). Four distinct atrophy subtypes were identified (temporoparietal, centrotemporal, frontal, and bilateral). Individuals with the centrotemporal subtype were younger at onset (median 5.5 years) than individuals with temporoparietal (median 11.5 years, p = 0.02) and frontal (median 6 years, p = 0.02) subtypes. Most severe neuropsychological impairment was observed for the temporoparietal and frontal subtypes. In the temporoparietal and frontal subtypes, atrophy occurred preferentially in hubs (r = -0.28, p = 0.006; r = -0.30, p = 0.02). Disease epicenter susceptibility was associated with higher cortical thickness (r = -0.57, p = 0.005), lower myelin content (r = 0.47, p = 0.02), lower cerebral blood flow (r = 0.42, p = 0.03), lower blood volume (r = 0.57, p = 0.006), and lower oxygen metabolism (r = 0.47, p = 0.01). Brain biopsies showing strong inflammation were taken from likely epicenters, whereas biopsies with weaker inflammation came from less likely epicenters (p = 0.04).

DISCUSSION

Using Rasmussen syndrome as a model, we validate imaging-based mapping of individual disease epicenters with histopathologic evidence. With further validation, network-based mapping of individual disease epicenters could potentially be used in Rasmussen syndrome to guide biopsy site selection, inform treatment decisions, and improve outcome prognoses.

A two-dimensional space of linguistic representations shared across individuals

Our ability to extract meaning from linguistic inputs and package ideas into word sequences is supported by a network of left-hemisphere frontal and temporal brain areas. Despite extensive research, previous attempts to discover differences among these language areas have not revealed clear dissociations or spatial organization. All areas respond similarly during controlled linguistic experiments as well as during naturalistic language comprehension. To search for finer-grained organizational principles of language processing, we applied data-driven decomposition methods to ultra-high-field (7T) fMRI responses from eight participants listening to 200 linguistically diverse sentences. Using a cross-validation procedure that identifies shared structure across individuals, we find that two components successfully generalize across participants, together accounting for about 32% of the explainable variance in brain responses to sentences. The first component corresponds to processing difficulty, and the second-to meaning abstractness; we formally support this interpretation through targeted behavioral experiments and information-theoretic measures. Furthermore, we find that the two components are systematically organized within frontal and temporal language areas, with the meaning-abstractness component more prominent in the temporal regions. These findings reveal an interpretable, low-dimensional, spatially structured representational basis for language processing, and advance our understanding of linguistic representations at a detailed, fine-scale organizational level.

How do brain regions specialised for concrete and abstract concepts align with functional brain networks? A neuroimaging meta-analysis

Identifying the brain regions that process concrete and abstract concepts is key to understanding the neural architecture of thought, memory and language. We review current theories of concreteness effects and test their neural predictions in a meta-analysis of 72 neuroimaging studies (1400 participants). Our analysis includes more than twice as many studies as previous meta-analyses, allowing for a more sensitive mapping of these effects across the brain. We also conducted a quantitative assessment of the degree to which concreteness effects aligned with a range of large-scale functional brain networks. Our results suggest that concrete and abstract concepts vary both in the information-processing modalities they engage and in the demands they place on cognitive control processes. Abstract concepts preferentially activated networks for social cognition (particularly for sentences), language and semantic control (particularly when presented as single words). Concrete concepts preferentially activated action processing regions when presented in sentences, though we found no evidence that they activated visual networks. Specialisation for both concept types was present in different parts of the default mode network (DMN), with effects dissociating along a social-spatial axis. Concrete concepts generated greater activation in a medial temporal DMN component, implicated in constructing mental models of spatial contexts and scenes. In contrast, abstract concepts showed greater activation in frontotemporal DMN regions involved in social and language processing. These results align with prior claims that generating models of situations and events is a core DMN function and indicate specialisation within DMN for different aspects of these models.

Normative structural connectome constrains spreading transient brain activity in generalized epilepsy

BACKGROUND

Genetic generalized epilepsy is characterized by transient episodes of spontaneous abnormal neural activity in anatomically distributed brain regions that ultimately propagate to wider areas. However, the connectome-based mechanisms shaping these abnormalities remain largely unknown. We aimed to investigate how the normative structural connectome constrains abnormal brain activity spread in genetic generalized epilepsy with generalized tonic-clonic seizure (GGE-GTCS).

METHODS

Abnormal transient activity patterns between individuals with GGE-GTCS (n = 97) and healthy controls (n = 141) were estimated from the amplitude of low-frequency fluctuations measured by resting-state functional MRI. The normative structural connectome was derived from diffusion-weighted images acquired in an independent cohort of healthy adults (n = 326). Structural neighborhood analysis was applied to assess the degree of constraints between activity vulnerability and structural connectome. Dominance analysis was used to determine the potential molecular underpinnings of these constraints. Furthermore, a network-based diffusion model was utilized to simulate the spread of pathology and identify potential disease epicenters.

RESULTS

Brain activity abnormalities among patients with GGE-GTCS were primarily located in the temporal, cingulate, prefrontal, and parietal cortices. The collective abnormality of structurally connected neighbors significantly predicted regional activity abnormality, indicating that white matter network architecture constrains aberrant activity patterns. Molecular fingerprints, particularly laminar differentiation and neurotransmitter receptor profiles, constituted key predictors of these connectome-constrained activity abnormalities. Network-based diffusion modeling effectively replicated transient pathological activity spreading patterns, identifying the limbic-temporal, dorsolateral prefrontal, and occipital cortices as putative disease epicenters. These results were robust across different clinical factors and individual patients.

CONCLUSIONS

Our findings suggest that the structural connectome shapes the spatial patterning of brain activity abnormalities, advancing our understanding of the network-level mechanisms underlying vulnerability to abnormal brain activity onset and propagation in GGE-GTCS.

Processes and measurements: a framework for understanding neural oscillations in field potentials

Various neuroscientific theories maintain that brain oscillations are important for neuronal computation, but opposing views claim that these macroscale dynamics are 'exhaust fumes' of more relevant processes. Here, we approach the question of whether oscillations are functional or epiphenomenal by distinguishing between measurements and processes, and by reviewing whether causal or inferentially useful links exist between field potentials, electric fields, and neurobiological events. We introduce a vocabulary for the role of brain signals and their underlying processes, demarcating oscillations as a distinct entity where both processes and measurements can exhibit periodicity. Leveraging this distinction, we suggest that electric fields, oscillating or not, are causally and computationally relevant, and that field potential signals can carry information even without causality.

Functionally specialized spectral organization of the resting human cortex

Ample studies across various neuroimaging modalities have suggested that the human cortex at rest is hierarchically organized along the spectral and functional axes. However, the relationship between the spectral and functional organizations of the human cortex remains largely unexplored. Here, we reveal the confluence of functional and spectral cortical organizations by examining the functional specialization in spectral gradients of the cortex. These spectral gradients, derived from functional magnetic resonance imaging data at rest using our temporal de-correlation method to enhance spectral resolution, demonstrate regional frequency biases. The grading of spectral gradients across the cortex - aligns with many existing brain maps - is found to be highly functionally specialized through discovered frequency-specific resting-state functional networks, functionally distinctive spectral profiles, and an intrinsic coordinate system that is functionally specialized. By demonstrating the functionally specialized spectral gradients of the cortex, we shed light on the close relation between functional and spectral organizations of the resting human cortex.

Disentangling the Switching Behavior in Functional Connectivity Dynamics in Autism Spectrum Disorder: Insights from Developmental Cohort Analysis and Molecular-Cellular Associations

Characterizing the transition or switching behavior between multistable brain states in functional connectivity dynamics (FCD) holds promise for uncovering the underlying neuropathology of Autism Spectrum Disorder (ASD). However, whether and how switching behaviors in FCD change in patients with developmental ASD, as well as their cellular and molecular basis, remains unexplored. This study develops a region-wise FCD switching index (RFSI) to investigate the drivers of FCD. This work finds that brain regions within the salience, default mode, and frontoparietal networks serve as abnormal drivers of FCD in ASD across different developmental stages. Additionally, changes in RFSI at different developmental stages of ASD correlated with transcriptomic profiles and neurotransmitter density maps. Importantly, the abnormal RFSI identifies in humans has also been observed in genetically edited ASD monkeys. Finally, single-nucleus RNA sequencing data from patients with developmental ASD are analyzed and aberrant switching behaviors in FCD may be mediated by somatostatin-expressing interneurons and altered differentiation patterns in astrocyte State2. In conclusion, this study provides the first evidence of abnormal drivers of FCD across different stages of ASD and their associated cellular and molecular mechanisms. These findings deepen the understanding of ASD neuropathology and offer valuable insights into treatment strategies.

Genetic Foundations of Inter-individual Neurophysiological Variability

Neurophysiological brain activity shapes cognitive functions and individual traits. Here, we investigated the extent to which individual neurophysiological properties are genetically determined and how these adult traits align with cortical gene expression patterns across development. Using task-free magnetoencephalography in monozygotic and dizygotic twins, as well as unrelated individuals, we found that neurophysiological traits were significantly more similar between monozygotic twins, indicating a genetic influence, although individual-specific variability remained predominant. These heritable brain dynamics were predominantly associated with genes involved in neurotransmission, expressed along a topographical gradient that mirrors psychological functions, including attention, planning, and emotional processes. Furthermore, the cortical expression patterns of genes associated with individual differentiation aligned most strongly with gene expression profiles observed during adulthood in previously published longitudinal datasets. These findings underscore a persistent genetic influence on neurophysiological activity, supporting individual cognitive and behavioral variability.

Disuse-driven plasticity in the human thalamus and putamen

Subcortical plasticity has mainly been studied using invasive electrophysiology in animals. Here, we leverage precision functional mapping (PFM) to study motor plasticity in the human subcortex during 2 weeks of upper-extremity immobilization with daily resting-state and motor task fMRI. We found previously that, in the cortex, limb disuse drastically impacts disused primary motor cortex functional connectivity (FC) and is associated with spontaneous fMRI pulses. It remains unknown whether disuse-driven plasticity pulses and FC changes are cortex specific or whether they could also affect movement-critical nodes in the thalamus and striatum. Tailored analysis methods now show spontaneous disuse pulses and FC changes in the dorsal posterior putamen and central thalamus (centromedian [CM], ventral-intermediate [VIM], and ventroposterior-lateral nuclei), representing a motor circuit-wide plasticity phenomenon. The posterior putamen effects suggest plasticity in stimulus-driven habit circuitry. Importantly, thalamic plasticity effects are focal to nuclei used as deep brain stimulation targets for essential tremor/Parkinson's disease (VIM) and epilepsy/coma (CM).

Cortical differences across psychiatric disorders and associated common and rare genetic variants

Genetic studies have identified common and rare variants increasing the risk for neurodevelopmental and psychiatric disorders (NPDs). These risk variants have also been shown to influence the structure of the cerebral cortex. However, it is unknown whether cortical differences associated with genetic variants are linked to the risk they confer for NPDs. To answer this question, we analyzed cortical thickness (CT) and surface area (SA) for common and rare variants associated with NPDs, in ~33000 individuals from the general population and clinical cohorts, as well as ENIGMA summary statistics for 8 NPDs. Rare and common genetic variants increasing risk for NPDs were preferentially associated with total SA, while NPDs were preferentially associated with mean CT. Larger effects on mean CT, but not total SA, were observed in NPD medicated subgroups. At the regional level, genetic variants were preferentially associated with effects in sensorimotor areas, while NPDs showed higher effects in association areas. We show that schizophrenia- and bipolar-disorder-associated SNPs show positive and negative effect sizes on SA suggesting that their aggregated effects cancel out in additive polygenic models. Overall, CT and SA differences associated with NPDs do not relate to those observed across individual genetic variants and may be linked with critical non-genetic factors, such as medication and the lived experience of the disorder.

Cortical Effects of Dopamine Replacement Account for Clinical Response Variability in Parkinson's Disease

BACKGROUND

Individual variability in clinical response to dopamine replacement therapy (DRT) is a key barrier to efficacious treatment for patients with Parkinson's disease (PD). A better understanding of the neurobiological sources of such interindividual differences is necessary to personalize DRT prescribing, inform future clinical interventions, and motivate translational research.

OBJECTIVE

One potential source of this variability is an unintended secondary activation of extra-nigrostriatal dopamine systems by DRT, particularly in the neocortex. Our goal was to determine the clinical effects of cortical dopamine system activation by DRT in patients with PD.

METHODS

We used pharmaco-magnetoencephalography data collected from patients with PD (NPD = 17, NHC = 20) before and after DRT to map their cortical neurophysiological responses to dopaminergic pharmacotherapy. By combining these DRT response maps with normative atlases of cortical dopamine system densities, we linked the variable enhancement of rhythmic cortical activity by DRT to dopamine-rich cortical regions and determined its clinical relevance.

RESULTS

We found beta-rhythmic responses to DRT in dopamine-rich regions of the cortex that are expressed variably across individuals. Importantly, patients who exhibited a larger dopaminergic beta cortical enhancement showed a smaller clinical improvement from DRT, indicating a potential source of individual variability in medication response for patients with PD.

CONCLUSIONS

We conclude that these findings inform our understanding of the dopaminergic basis of neurophysiological variability often seen in patients with PD, and indicate that our methodological approach may be useful for data-driven contextualization of medication effects on cortical neurophysiology in future research and clinical applications. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Disrupted Hierarchical Functional Brain Organization in Affective and Psychotic Disorders: Insights from Functional Brain Gradients

Patients with psychosis and depression show widespread alterations in brain resting-state functional connectivity (rs-FC), affecting both sensory and higher-order brain regions. In this study, we investigate disruptions in the hierarchical organization of brain functional networks in patients with psychotic and affective disorders. We derived functional brain gradients, low dimensional representations of rs-FC that capture cortical hierarchy, in a large patient sample including clinical high-risk for psychosis (CHR-P) patients, recent-onset psychosis (ROP) patients, recent-onset depression (ROD) patients, and healthy controls (HC). We examined regional alterations, network-level alterations and functional differentiation and their relationship to clinical symptoms. In addition, we linked case-control differences to receptor expression maps to explore underlying neurobiological mechanisms. All patient groups exhibited alterations in the visual-to-sensorimotor gradient, while only ROP patients showed alterations in the association-to-sensory gradient. CHR-P and ROP patients exhibited lower values in the ventral attention network. Additionally, patients combined showed higher values in the somatomotor network, a reduced gradient range and altered between-network dispersion. ROD showed reduced within-network dispersion in the attentional networks and a reduced range. Correlational analysis revealed weak associations of gradient measures with functioning, visual dysfunctions and cognition. Furthermore case-control differences showed associations to receptor expression maps, suggesting the involvement of neurotransmitter systems in these disruptions. Our findings reveal transdiagnostic and disease-specific alterations of hierarchical brain organization. These alterations indicate deficits in functional integration across psychiatric diseases, highlighting the role of attentional and sensory networks in disease processes.

Altered integrated and segregated states in cocaine use disorder

Introduction

Cocaine use disorder (CUD) is a chronic brain condition that severely impairs cognitive function and behavioral control. The neural mechanisms underlying CUD, particularly its impact on brain integration-segregation dynamics, remain unclear.

Methods

In this study, we integrate dynamic functional connectivity and graph theory to compare the brain state properties of healthy controls and CUD patients.

Results

We find that CUD influences both integrated and segregated states, leading to distinct alterations in connectivity patterns and network properties. CUD disrupts connectivity involving the default mode network, frontoparietal network, and subcortical structures. In addition, integrated states show distinct sensorimotor connectivity alterations, while segregated states exhibit significant alterations in frontoparietal-subcortical connectivity. Regional connectivity alterations among both states are significantly associated with MOR and H3 receptor distributions, with integrated states showing more receptor-connectivity couplings. Furthermore, CUD alters the positive-negative correlation balance, increases functional complexity at threshold 0, and reduces mean betweenness centrality and modularity in the critical subnetworks. Segregated states in CUD exhibit lower normalized clustering coefficients and functional complexity at a threshold of 0.3. We also identify network properties in integrated states that are reliably correlated with cocaine consumption patterns.

Discussion

Our findings reveal temporal effects of CUD on brain integration and segregation, providing novel insights into the dynamic neural mechanisms underlying cocaine addiction.

Molecular, physiological and functional features underlying antipsychotic medication use related cortical thinning

Use of antipsychotic medication is related to thinning of the cerebral cortex, but the underlying mechanisms of this effect remain largely unknown. Here, we investigated potential mechanisms across multiple levels of description by comparing antipsychotic medication related cortical thinning to atlases of normative neurotransmitter distributions, structural and functional organization of the brain, and meta-analyses of functional activation from the Neurosynth database. We first analyzed a single-site discovery sample of patients (N = 131) with early psychosis for whom antipsychotic related cortical thinning was estimated based on lifetime exposure to antipsychotics. Findings were replicated using data from a large (N ≥ 2168) ENIGMA meta-analysis on schizophrenia patients. We discovered that antipsychotic related cortical thinning is associated with a number of neurotransmitter systems, most notably the serotonin system, as well as physiological measures, functional networks and neural oscillatory power distributions typical for regions subserving higher cognition. At the functional level, antipsychotic related cortical thinning affects regions involved in executive function and motivation, but not perception. These results show how molecular, physiological, and large-scale functional patterns may underlie antipsychotic related cortical thinning.

Estimating Brain Similarity Networks with Diffusion MRI

Structural similarity has emerged as a promising tool in mapping the network organization of an individual, living human brain. Here, we propose diffusion similarity networks (DSNs), which employ rotationally invariant spherical harmonic features derived from diffusion magnetic resonance imaging (dMRI), to map gray matter structural organization. Compared to prior approaches, DSNs showed clearer laminar, cytoarchitectural, and micro-architectural organization; greater sensitivity to age, cognition, and sex; higher heritability in a large dataset of healthy young adults; and straightforward extension to non-cortical regions. We show DSNs are correlated with functional, structural, and gene expression connectomes and their gradients align with the sensory-fugal and sensorimotor-association axes of the cerebral cortex, including neuronal oscillatory dynamics, metabolism, immunity, and dopaminergic and glutaminergic receptor densities. DSNs can be easily integrated into conventional dMRI analysis, adding information complementary to structural white matter connectivity, and could prove useful in investigating a wide array of neurological and psychiatric conditions.

Cortical structure in relation to empathy and psychopathy in 800 incarcerated men

Background

Reduced affective empathy is a hallmark of psychopathy, which incurs major interpersonal and societal costs. Advancing our neuroscientific understanding of this reduction and other psychopathic traits is crucial for improving their treatment.

Methods

In 804 incarcerated adult men, we administered the Perspective Taking (IRI-PT) and Empathic Concern (IRI-EC) subscales of the Interpersonal Reactivity Index, Hare Psychopathy Checklist-Revised (PCL-R; two factors), and T1-weighted MRI to quantify cortical thickness (CT) and surface area (SA). We also included the male sample of the Human Connectome Project (HCP; N = 501) to replicate patterns of macroscale structural organization.

Results

Factor 1 (Interpersonal/Affective) uniquely negatively related to IRI-EC, while Factor 2 (Lifestyle/Antisocial) uniquely negatively related to IRI-PT. Cortical structure did not relate to either IRI subscale, although there was effect-size differentiation by microstructural class and/or functional network. CT related to Factor 1 (mostly positively), SA related to both factors (only positively), and both cortical indices demonstrated out-of-sample predictive utility for Factor 1. The high-psychopathy group (N = 178) scored uniquely lower on IRI-EC while having increased SA (but not CT). Regionally, these SA increases localized primarily in the paralimbic class and somatomotor network, with meta-analytic task-based activations corroborating affective-sensory importance. High psychopathy also showed "compressed" global and/or network-level organization of both cortical indices, and this organization in the total sample replicated in HCP. All findings accounted for age, IQ, and/or total intracranial volume.

Conclusions

Psychopathy had negative relationships with affective empathy and positive relationships with paralimbic/somatomotor SA, highlighting the role of affect and sensation.

Generalizable and transportable resting-state neural signatures characterized by functional networks, neurotransmitters, and clinical symptoms in autism

Autism spectrum disorder (ASD) is a lifelong condition with elusive biological mechanisms. The complexity of factors, including inter-site and developmental differences, hinders the development of a generalizable neuroimaging classifier for ASD. Here, we developed a classifier for ASD using a large-scale, multisite resting-state fMRI dataset of 730 Japanese adults, aiming to capture neural signatures that reflect pathophysiology at the functional network level, neurotransmitters, and clinical symptoms of the autistic brain. Our adult ASD classifier was successfully generalized to adults in the United States, Belgium, and Japan. The classifier further demonstrated its successful transportability to children and adolescents. The classifier contained 141 functional connections (FCs) that were important for discriminating individuals with ASD from typically developing controls. These FCs and their terminal brain regions were associated with difficulties in social interaction and dopamine and serotonin, respectively. Finally, we mapped attention-deficit/hyperactivity disorder (ADHD), schizophrenia (SCZ), and major depressive disorder (MDD) onto the biological axis defined by the ASD classifier. ADHD and SCZ, but not MDD, were located proximate to ASD on the biological dimensions. Our results revealed functional signatures of the ASD brain, grounded in molecular characteristics and clinical symptoms, achieving generalizability and transportability applicable to the evaluation of the biological continuity of related diseases.

Prefrontal-hippocampal pathways underlying adolescent resilience

The prefrontal-hippocampal pathways are integral to memory suppression, facilitating positive and adaptative responses following traumatic events. However, the role of these circuits in promoting resilience among adolescents remains largely unknown. This study used structural similarity analysis of MRI-based gray matter volume (GMV) to map connectivity networks centered on the hippocampus, investigating whether structural similarity between prefrontal regions and hippocampus were related to resilience in a cohort of 145 adolescents. Additionally, spatial correlation analyses of resilience-related structural similarity network and neurotransmitter distribution maps were conducted to identify molecular adaptations within prefrontal-hippocampal circuits associated with resilience. The results showed that higher resilience levels were correlated with stronger structural similarity between the prefrontal areas (i.e., middle frontal gyrus and orbitofrontal cortex) and hippocampus. Furthermore, the serotonergic neurotransmitter system, which modulates neural oscillations in prefrontal-hippocampal pathways, appears to be associated with resilience. The current findings suggest that structural and molecular adaptations within prefrontal-hippocampal circuits, which are implicated in the suppression of intrusive, unwanted memories, may foster resilience in young people. These insights advance our knowledge of the neurobiological markers of resilience, paving the way for more targeted and effective therapeutic interventions to bolster resilience and mitigate adverse outcomes in developmental populations.

A network correspondence toolbox for quantitative evaluation of novel neuroimaging results

The brain can be decomposed into large-scale functional networks, but the specific spatial topographies of these networks and the names used to describe them vary across studies. Such discordance has hampered interpretation and convergence of research findings across the field. We have developed the Network Correspondence Toolbox (NCT) to permit researchers to examine and report spatial correspondence between their novel neuroimaging results and multiple widely used functional brain atlases. We provide several exemplar demonstrations to illustrate how researchers can use the NCT to report their own findings. The NCT provides a convenient means for computing Dice coefficients with spin test permutations to determine the magnitude and statistical significance of correspondence among user-defined maps and existing atlas labels. The adoption of the NCT will make it easier for network neuroscience researchers to report their findings in a standardized manner, thus aiding reproducibility and facilitating comparisons between studies to produce interdisciplinary insights.

Network-based Molecular Constraints on in vivo Synaptic Density Alterations in Schizophrenia

Converging neuroimaging, genetic, and post-mortem evidence show a fundamental role of synaptic deficits in schizophrenia pathogenesis. However, the underlying molecular and cellular mechanisms that drive the onset and progression of synaptic pathology remain to be established. Here, we used synaptic density positron emission tomography (PET) imaging using the [11C]UCB-J radiotracer to reveal a prominent widespread pattern (p FWE < 0.05) of lower synaptic density in individuals with schizophrenia (n=29), compared to a large sample of healthy controls (n=93). We found that the spatial pattern of lower synaptic density in schizophrenia is spatially aligned (r cca = 0.67; p < 0.001) with higher normative distributions of GABAA/BZ, 5HT1B, 5HT2A, and 5HT6, and lower levels of CB1 and 5HT1A. Competing neighborhood deformation network models revealed that regional synaptic pathology strongly correlated with estimates predicted using a model constrained by both interregional structural connectivity and molecular similarity (.42 < r < .61; p FWE < 0.05). These data suggest that synaptic pathology in schizophrenia is jointly constrained by both global axonal connectivity and local molecular vulnerability. Simulation-based network diffusion models were used to identify regions that may represent the initial sources of pathology, nominating left prefrontal areas (p FWE < 0.05) as potential foci from which synaptic pathology initiates and propagates to molecularly similar areas. Overall, our findings provide in vivo evidence for widespread deficit in synaptic density in schizophrenia that is jointly constrained by axonal connectivity and molecular similarity between regions, and that synaptic deficits spread from initial source regions to axonally connected and molecularly similar territories.

Neurotransmitters' white matter mapping unveils the neurochemical fingerprints of stroke

Distinctive patterns of brain neurotransmission frame determinant circuits for behavior. Understanding the relationship between their damage and the cognitive impairment provoked by brain lesions could provide insights into the pathophysiology and therapeutics of disabling disorders, like stroke. Yet, the challenges of neurotransmitter circuits mapping in vivo have hampered this investigation. Here, we developed an MRI white matter atlas of neurotransmitter circuits and created a method to chart how stroke damages neurotransmitter systems, which distinguishes pre and postsynaptic disruption. Our model, trained and tested in two large stroke patient samples, identified eight clusters with different neurochemical patterns. The associations with patients' cognitive profiles were scarce, denoting that a particular cognitive deficit might have finer underlying neurochemical disturbances that are unfit to the granularity of our analyses. These findings depict stroke neurochemical diaschisis patterns, provide insights into stroke cognitive deficits and potential treatments, and open a new window for tailored neurotransmitter modulation.

Major depressive disorder on a neuromorphic continuum

The heterogeneity of major depressive disorder (MDD) has hindered clinical translation and neuromarker identification. Biotyping facilitates solving the problems of heterogeneity, by dissecting MDD patients into discrete subgroups. However, interindividual variations suggest that depression may be conceptualized as a "continuum," rather than as a "category." We use a Bayesian model to decompose structural MRI features of MDD patients from a multisite cross-sectional cohort into three latent disease factors (spatial pattern) and continuum factor compositions (individual expression). The disease factors are associated with distinct neurotransmitter receptors/transporters obtained from open PET sources. Increases cortical thickness in sensory and decreases in orbitofrontal cortices (Factor 1) associate with norepinephrine and 5-HT2A density, decreases in the cingulo-opercular network and subcortex (Factor 2) associate with norepinephrine and 5-HTT density, and increases in social and affective brain systems (Factor 3) relate to 5-HTT density. Disease factor patterns can also be used to predict depressive symptom improvement in patients from the longitudinal cohort. Moreover, individual factor expressions in MDD are stable over time in a longitudinal cohort, with differentially expressed disease controls from a transdiagnostic cohort. Collectively, our data-driven disease factors reveal that patients with MDD organize along continuous dimensions that affect distinct sets of regions.

Brain aging shows nonlinear transitions, suggesting a midlife "critical window" for metabolic intervention

Understanding the key drivers of brain aging is essential for effective prevention and treatment of neurodegenerative diseases. Here, we integrate human brain and physiological data to investigate underlying mechanisms. Functional MRI analyses across four large datasets (totaling 19,300 participants) show that brain networks not only destabilize throughout the lifetime but do so along a nonlinear trajectory, with consistent temporal "landmarks" of brain aging starting in midlife (40s). Comparison of metabolic, vascular, and inflammatory biomarkers implicate dysregulated glucose homeostasis as the driver mechanism for these transitions. Correlation between the brain's regionally heterogeneous patterns of aging and gene expression further supports these findings, selectively implicating GLUT4 (insulin-dependent glucose transporter) and APOE (lipid transport protein). Notably, MCT2 (a neuronal, but not glial, ketone transporter) emerges as a potential counteracting factor by facilitating neurons' energy uptake independently of insulin. Consistent with these results, an interventional study of 101 participants shows that ketones exhibit robust effects in restabilizing brain networks, maximized from ages 40 to 60, suggesting a midlife "critical window" for early metabolic intervention.

Associations between neuromelanin depletion and cortical rhythmic activity in Parkinson's disease

Parkinson's disease (PD) is marked by the death of neuromelanin-rich dopaminergic and noradrenergic cells in the substantia nigra (SN) and the locus coeruleus (LC), respectively, resulting in motor and cognitive impairments. Although SN dopamine dysfunction has clear neurophysiological effects, the association of reduced LC norepinephrine signalling with brain activity in PD remains to be established. We used neuromelanin-sensitive T1-weighted MRI (PD, n = 58; healthy control, n = 27) and task-free magnetoencephalography (PD, n = 58; healthy control, n = 65) to identify neuropathophysiological factors related to the degeneration of the LC and SN in patients with PD. We found pathological increases in rhythmic alpha-band (8-12 Hz) activity in patients with decreased LC neuromelanin, which were more strongly associated in patients with worse attentional impairments. This negative alpha-band-LC neuromelanin relationship is strongest in fronto-motor cortices, where alpha-band activity is inversely related to attention scores. Using neurochemical co-localization analyses with normative atlases of neurotransmitter transporters, we also show that this effect is more pronounced in regions with high densities of norepinephrine transporters. These observations support a noradrenergic association between LC integrity and alpha-band activity. Our data also show that rhythmic beta-band (15-29 Hz) activity in the left somatomotor cortex decreases with lower levels of SN neuromelanin; the same regions where beta activity reflects axial motor symptoms. Together, our findings clarify the association of well-documented alterations of rhythmic neurophysiology in PD with cortical and subcortical neurochemical systems. Specifically, attention-related alpha-band activity is related to dysfunction of the noradrenergic system, and beta activity with relevance to motor impairments reflects dopaminergic dysfunction.

The control costs of human brain dynamics

The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using the network control theory, here, we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from the positron emission tomography, providing insight into the bioenergetic footing of resting-state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Temporal Lobe Epilepsy Perturbs the Brain-Wide Excitation-Inhibition Balance: Associations with Microcircuit Organization, Clinical Parameters, and Cognitive Dysfunction

Excitation-inhibition (E/I) imbalance is theorized as a key mechanism in the pathophysiology of epilepsy, with ample research focusing on elucidating its cellular manifestations. However, few studies investigate E/I imbalance at the macroscale, whole-brain level, and its microcircuit-level mechanisms and clinical significance remain incompletely understood. Here, the Hurst exponent, an index of the E/I ratio, is computed from resting-state fMRI time series, and microcircuit parameters are simulated using biophysical models. A broad decrease in the Hurst exponent is observed in pharmaco-resistant temporal lobe epilepsy (TLE), suggesting more excitable network dynamics. Connectome decoders point to temporolimbic and frontocentral cortices as plausible network epicenters of E/I imbalance. Furthermore, computational simulations reveal that enhancing cortical excitability in TLE reflects atypical increases in recurrent connection strength of local neuronal ensembles. Mixed cross-sectional and longitudinal analyses show stronger E/I ratio elevation in patients with longer disease duration, more frequent electroclinical seizures as well as interictal epileptic spikes, and worse cognitive functioning. Hurst exponent-informed classifiers discriminate patients from healthy controls with high accuracy (72.4% [57.5%-82.5%]). Replicated in an independent dataset, this work provides in vivo evidence of a macroscale shift in E/I balance in TLE patients and points to progressive functional imbalances that relate to cognitive decline.

Cerebral Blood Flow Changes and Their Spatial Correlations With GABAa and Dopamine-D1 Receptor Explaining Individual Differences in Chronic Insomnia and the Therapeutic Effects of Acupuncture

This study integrated neuroimaging and neurochemistry data to explore brain mechanisms in chronic insomnia disorder (CID) and the neuromodulatory effects of acupuncture. We analyzed a cross-sectional arterial spin labeling (ASL) dataset (N = 197) of CID patients and healthy controls to identify cerebral blood flow (CBF) changes. Additionally, a longitudinal ASL dataset (N = 44) examined CBF changes in CID patients after a 4-week acupuncture treatment or on a waitlist. We then assessed the impact of 19 neurotransmitter receptors/transporters on these CBF alterations. In cross-sectional comparisons, CID patients exhibited increased CBF in cortical areas and decreased CBF in subcortical regions, correlating with insomnia severity. In longitudinal comparisons, acupuncture treatment enhanced subcortical CBF and alleviated insomnia symptoms, changes not observed in the waitlist group. The left putamen was identified as an overlapping subcortical region involved in both CID-related changes and post-treatment alterations. Moreover, the CBF patterns induced by acupuncture negatively correlated with the abnormal patterns in CID patients, and both were significantly associated with GABAa and dopamine-D1 receptor densities. The observed decrease in CBF in the left putamen could potentially serve as a neural biomarker for CID, while acupuncture may alleviate insomnia symptoms by increasing CBF in this region, potentially through the modulation of GABAa and D1 receptor expressions.

A novel method for functional brain networks based on static cerebral blood flow

Cerebral blood flow (CBF) offers a quantitative and reliable measurement for brain activity and is increasingly used to study functional networks. However, current methods evaluate inter-regional relations mainly based on CBF temporal dynamics, which suffers from low signal-to-noise ratio and poor temporal resolution. Here we proposed a method to construct functional brain networks by estimating shape similarity (index by Jensen-Shannon divergence) in probability distributions of regional static CBF measured by arterial spin labeling perfusion imaging over a scanning period. Based on CBF data of 30 healthy participants from 10 visits, we found that the CBF networks exhibited non-trivial topological features (e.g., small-world organization, modular architecture, and hubs) and showed low-to-fair test-retest reliability and high between-subject consistency. We further found that interregional CBF similarities were depended on anatomical distance and differed between high- and lower-order subnetworks. Moreover, interregional CBF similarities within high-order subnetworks showed significantly lower reliability than those within low-order subnetworks. Finally, we showed that nodal degree of the CBF networks were related to regional sizes and CBF levels and spatially aligned with maps of the dopamine transporter and metabolic glutamate receptor 5 intensities, expression levels of genes primarily enriched in cholesterol-related pathways and endothelial cells, and meta-analytic activations related to memory, language, and executive function. Altogether, our proposed method provide a novel, relatively reliable, and neurobiologically meaningful means to study functional network organization of the human brain.

REI-Net: A Reference Electrode Standardization Interpolation Technique Based 3D CNN for Motor Imagery Classification

High-quality scalp EEG datasets are extremely valuable for motor imagery (MI) analysis. However, due to electrode size and montage, different datasets inevitably experience channel information loss, posing a significant challenge for MI decoding. A 2D representation that focuses on the time domain may loss the spatial information in EEG. In contrast, a 3D representation based on topography may suffer from channel loss and introduce noise through different padding methods. In this paper, we propose a framework called Reference Electrode Standardization Interpolation Network (REI-Net). Through an interpolation of 3D representation, REI-Net retains the temporal information in 2D scalp EEG while improving the spatial resolution within a certain montage. Additionally, to overcome the data variability caused by individual differences, transfer learning is employed to enhance the decoding robustness. Our approach achieves promising performance on two widely-recognized MI datasets, with an accuracy of 77.99% on BCI-C IV-2a and an accuracy of 63.94% on Kaya2018. The proposed algorithm outperforms the SOTAs leading to more accurate and robust results.

Benchmarking macaque brain gene expression for horizontal and vertical translation

The spatial patterning of gene expression shapes cortical organization and function. The macaque is a fundamental model organism in neuroscience, but the translational potential of macaque gene expression rests on the assumption that it is a good proxy for patterns of corresponding proteins (vertical translation) and for patterns of orthologous human genes (horizontal translation). Here, we systematically benchmark regional gene expression in macaque cortex against (i) macaque cortical receptor density and in vivo and ex vivo microstructure and (ii) human cortical gene expression. We find moderate cortex-wide correspondence between macaque gene expression and protein density, which improves by considering layer-specific gene expression. Half of the examined genes exhibit significant correlation between macaque and human across the cortex. Interspecies correspondence of gene expression is greater in unimodal than in transmodal cortex, recapitulating evolutionary cortical expansion and gene-protein correspondence in the macaque. These results showcase the potential and limitations of macaque cortical transcriptomics for translational discovery within and across species.

GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization

Since resting-state networks were first observed using magnetic resonance imaging (MRI), their cognitive relevance has been widely suggested. However, to date, the empirical cognitive characterization of these networks has been limited. The present study introduces the Groupe d'Imagerie Neurofonctionnelle Network Atlas, a comprehensive brain atlas featuring 33 resting-state networks. Based on the resting-state data of 1812 participants, the atlas was developed by classifying independent components extracted individually, ensuring consistent between-subject detection. We further explored the cognitive relevance of each GINNA network using Neurosynth-based meta-analytic decoding and generative null hypothesis testing. Significant cognitive terms for each network were then synthesized into appropriate cognitive processes through the consensus of six authors. The GINNA atlas showcases a diverse range of topological profiles, reflecting a broad spectrum of the known human cognitive repertoire. The processes associated with each network are named according to the standard Cognitive Atlas ontology, thus providing opportunities for empirical validation.

The role of structural connectivity on brain function through a Markov model of signal transmission

Structure determines function. However, this universal theme in biology has been surprisingly difficult to observe in human brain neuroimaging data. Here, we link structure to function by hypothesizing that brain signals propagate as a Markovian process on an underlying structure. We focus on a metric called commute time: the average number of steps for a random walker to go from region A to B and then back to A. Commute times based on white matter tracts from diffusion MRI exhibit an average ± standard deviation Spearman correlation of -0.26 ± 0.08 with functional MRI connectivity data across 434 UK Biobank individuals and -0.24 ± 0.06 across 400 HCP Young Adult brain scans. The correlation increases to -0.36 ± 0.14 and to -0.32 ± 0.12 when the principal contributions of both commute time and functional connectivity are compared for both datasets. The observed weak but robust correlations provide evidence of a relationship, albeit restricted, between neuronal connectivity and brain function. The correlations are stronger by 33% compared to broadly used communication measures such as search information and communicability. The difference further widens to a factor of 5 when commute times are correlated to the principal mode of functional connectivity from its eigenvalue decomposition. Overall, the study points to the utility of commute time to account for the role of polysynaptic (indirect) connectivity underlying brain function.

Transcriptional and Neurochemical Signatures of Cerebral Blood Flow Alterations in Individuals With Schizophrenia or at Clinical High Risk for Psychosis

BACKGROUND

The brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behavior. Understanding relationships across these scales may be fundamental to advancing understanding of brain function in health and disease. Recent neuroimaging research has shown that functional brain alterations that are associated with schizophrenia spectrum disorders (SSDs) are already present in young adults at clinical high risk for psychosis (CHR-P), but the cellular and molecular determinants of these alterations remain unclear.

METHODS

Here, we used regional cerebral blood flow (rCBF) data from 425 individuals (122 with an SSD compared with 116 healthy control participants [HCs] and 129 individuals at CHR-P compared with 58 HCs) and applied a novel pipeline to integrate brainwide rCBF case-control maps with publicly available transcriptomic data (17,205 gene maps) and neurotransmitter atlases (19 maps) from 1074 healthy volunteers.

RESULTS

We identified significant correlations between astrocyte, oligodendrocyte, oligodendrocyte precursor cell, and vascular leptomeningeal cell gene modules for both SSD and CHR-P rCBF phenotypes. Additionally, endothelial cell genes were correlated in SSD, and microglia in CHR-P. Receptor distribution significantly predicted case-control rCBF differences, with dominance analysis highlighting dopamine (D1, D2, dopamine transporter), acetylcholine (VAChT, M1), gamma-aminobutyric acid A (GABAA), and glutamate (NMDA) receptors as key predictors for SSD (R2adjusted = 0.58, false discovery rate [FDR]-corrected p < .05) and CHR-P (R2adjusted = 0.6, pFDR < .05) rCBF phenotypes. These associations were primarily localized in subcortical regions and implicate cell types involved in stress response and inflammation, alongside specific neuroreceptor systems, in shared and distinct rCBF phenotypes in psychosis.

CONCLUSIONS

Our findings underscore the value of integrating multiscale data as a promising hypothesis-generating approach toward decoding biological pathways involved in neuroimaging-based psychosis phenotypes, potentially guiding novel interventions.

Identifying neurobiological heterogeneity in clinical high-risk psychosis: a data-driven biotyping approach using resting-state functional connectivity

To explore the neurobiological heterogeneity within the Clinical High-Risk (CHR) for psychosis population, this study aimed to identify and characterize distinct neurobiological biotypes within CHR using features from resting-state functional networks. A total of 239 participants from the Shanghai At Risk for Psychosis (SHARP) program were enrolled, consisting of 151 CHR individuals and 88 matched healthy controls (HCs). Functional connectivity (FC) features that were correlated with symptom severity were subjected to the single-cell interpretation through multikernel learning (SIMLR) algorithm in order to identify latent homogeneous subgroups. The cognitive function, clinical symptoms, FC patterns, and correlation with neurotransmitter systems of biotype profiles were compared. Three distinct CHR biotypes were identified based on 646 significant ROI-ROI connectivity features, comprising 29.8%, 19.2%, and 51.0% of the CHR sample, respectively. Despite the absence of overall FC differences between CHR and HC groups, each CHR biotype demonstrated unique FC abnormalities. Biotype 1 displayed augmented somatomotor connection, Biotype 2 shown compromised working memory with heightened subcortical and network-specific connectivity, and Biotype 3, characterized by significant negative symptoms, revealed extensive connectivity reductions along with increased limbic-subcortical connectivity. The neurotransmitter correlates differed across biotypes. Biotype 2 revealed an inverse trend to Biotype 3, as increased neurotransmitter concentrations improved functional connectivity in Biotype 2 but reduced it in Biotype 3. The identification of CHR biotypes provides compelling evidence for the early manifestation of heterogeneity within the psychosis spectrum, suggesting that distinct pathophysiological mechanisms may underlie these subgroups.

Geometric influences on the regional organization of the mammalian brain

The mammalian brain is comprised of anatomically and functionally distinct regions. Substantial work over the past century has pursued the generation of ever-more accurate maps of regional boundaries, using either expert judgement or data-driven clustering of functional, connectional, and/or architectonic properties. However, these approaches are often purely descriptive, have limited generalizability, and do not elucidate the underlying generative mechanisms that shape the regional organization of the brain. Here, we develop a novel approach that leverages a simple, hierarchical principle for generating a multiscale parcellation of any brain structure in any mammalian species using only its geometry. We show that this approach yields regions at any resolution scale that are more homogeneous than those defined in nearly all existing benchmark brain parcellations in use today across hundreds of anatomical, functional, cellular, and molecular brain properties measured in humans, macaques, marmosets, and mice. We additionally show how our method can be generalized to previously unstudied mammalian species for which no parcellations exist. Finally, we demonstrate how our approach captures the essence of a simple, hierarchical reaction-diffusion mechanism, in which the geometry of a brain structure shapes the spatial expression of putative patterning molecules linked to the formation of distinct regions through development. Our findings point to a highly conserved and universal influence of geometry on the regional organization of the mammalian brain.

Metastability demystified - the foundational past, the pragmatic present and the promising future

Healthy brain function depends on balancing stable integration between brain areas for effective coordinated functioning, with coexisting segregation that allows subsystems to express their functional specialization. Metastability, a concept from the dynamical systems literature, has been proposed as a key signature that characterizes this balance. Building on this principle, the neuroscience literature has leveraged the phenomenon of metastability to investigate various aspects of brain function in health and disease. However, this body of work often uses the notion of metastability heuristically, and sometimes inaccurately, making it difficult to navigate the vast literature, interpret findings and foster further development of theoretical and experimental methodologies. Here, we provide a comprehensive review of metastability and its applications in neuroscience, covering its scientific and historical foundations and the practical measures used to assess it in empirical data. We also provide a critical analysis of recent theoretical developments, clarifying common misconceptions and paving the road for future developments.

Convergent functional effects of antidepressants in major depressive disorder: a neuroimaging meta-analysis

BACKGROUND

Neuroimaging studies have provided valuable insights into the macroscale impacts of antidepressants on brain functions in patients with major depressive disorder. However, the findings of individual studies are inconsistent. Here, we aimed to provide a quantitative synthesis of the literature to identify convergence of the reported findings at both regional and network levels and to examine their associations with neurotransmitter systems.

METHODS

Through a comprehensive search in PubMed and Scopus databases, we reviewed 5258 abstracts and identified 36 eligible functional neuroimaging studies on antidepressant effects in major depressive disorder. Activation likelihood estimation was used to investigate regional convergence of the reported foci of antidepressant effects, followed by functional decoding and connectivity mapping of the convergent clusters. Additionally, utilizing group-averaged data from the Human Connectome Project, we assessed convergent resting-state functional connectivity patterns of the reported foci. Next, we compared the convergent circuit with the circuits targeted by transcranial magnetic stimulation therapy. Last, we studied the association of regional and network-level convergence maps with selected neurotransmitter receptors/transporters maps.

RESULTS

No regional convergence was found across foci of treatment-associated alterations in functional imaging. Subgroup analysis in the Treated > Untreated contrast revealed a convergent cluster in the left dorsolateral prefrontal cortex, which was associated with working memory and attention behavioral domains. Moreover, we found network-level convergence of the treatment-associated alterations in a circuit more prominent in the frontoparietal areas. This circuit was co-aligned with circuits targeted by "anti-subgenual" and "Beam F3" transcranial magnetic stimulation therapy. We observed no significant correlations between our meta-analytic findings with the maps of neurotransmitter receptors/transporters.

CONCLUSION

Our findings highlight the importance of the frontoparietal network and the left dorsolateral prefrontal cortex in the therapeutic effects of antidepressants, which may relate to their role in improving executive functions and emotional processing.

Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition

The human cerebral cortex exhibits intricate interareal functional synchronization at the macroscale, with substantial individual variability in these functional connections. However, the spatial organization of functional connectivity (FC) variability across the human connectome edges and its significance in cognitive development remain unclear. Here, we identified a connectional axis in the edge-level FC variability. The variability declined continuously along this axis from within-network to between-network connections, and from the edges linking association networks to those linking the sensorimotor and association networks. This connectional axis of functional variability is associated with spatial pattern of structural connectivity variability. Moreover, the connectional variability axis evolves in youth with an increasing flatter axis slope. We also observed that the slope of connectional variability axis was positively related to the performance in the higher-order cognition. Together, our results reveal a connectional axis in functional variability that is linked with structural connectome variability, refines during development, and is relevant to cognition.

Unique Functional Neuroimaging Signatures of Genetic Versus Clinical High Risk for Psychosis

BACKGROUND

22q11.2 deletion syndrome (22qDel) is a copy number variant that is associated with psychosis and other neurodevelopmental disorders. Adolescents who are at clinical high risk for psychosis (CHR) are identified based on the presence of subthreshold psychosis symptoms. Whether common neural substrates underlie these distinct high-risk populations is unknown. We compared functional brain measures in 22qDel and CHR cohorts and mapped the results to biological pathways.

METHODS

We analyzed 2 large multisite cohorts with resting-state functional magnetic resonance imaging data: 1) a 22qDel cohort (n = 164, 47% female) and typically developing (TD) control participants (n = 134, 56% female); and 2) a cohort of CHR individuals (n = 240, 41% female) and TD control participants (n = 149, 46% female) from the NAPLS-2 (North American Prodrome Longitudinal Study-2). We computed global brain connectivity (GBC), local connectivity (LC), and brain signal variability (BSV) across cortical regions and tested case-control differences for 22qDel and CHR separately. Group difference maps were related to published brain maps using autocorrelation-preserving permutation.

RESULTS

BSV, LC, and GBC were significantly disrupted in individuals with 22qDel compared with TD control participants (false discovery rate-corrected q < .05). Spatial maps of BSV and LC differences were highly correlated with each other, unlike GBC. In the CHR group, only LC was significantly altered versus the control group, with a different spatial pattern than the 22qDel group. Group differences mapped onto biological gradients, with 22qDel effects being strongest in regions with high predicted blood flow and metabolism.

CONCLUSIONS

22qDel carriers and CHR individuals exhibited different effects on functional magnetic resonance imaging temporal variability and multiscale functional connectivity. In 22qDel carriers, strong and convergent disruptions in BSV and LC that were not seen in CHR individuals suggest distinct functional brain alterations.

Atlas of plasma metabolic markers linked to human brain morphology

Background

Metabolic processes form the basis of the development, functioning and maintenance of the brain. Despite accumulating evidence of the vital role of metabolism in brain health, no study to date has comprehensively investigated the link between circulating markers of metabolic activity and in vivo brain morphology in the general population.

Methods

We performed uni- and multivariate regression on metabolomics and MRI data from 24,940 UK Biobank participants, to estimate the individual and combined associations of 249 circulating metabolic markers with 91 measures of global and regional cortical thickness, surface area and subcortical volume. We investigated similarity of the identified spatial patterns with brain maps of neurotransmitters, and used Mendelian randomization to uncover causal relationships between metabolites and the brain.

Results

Intracranial volume and total surface area were highly significantly associated with circulating lipoproteins and glycoprotein acetyls, with correlations up to .15. There were strong regional associations of individual markers with mixed effect directions, with distinct patterns involving frontal and temporal cortical thickness, brainstem and ventricular volume. Mendelian randomization provided evidence of bidirectional causal effects, with the majority of markers affecting frontal and temporal regions.

Discussion

The results indicate strong bidirectional causal relationships between circulating metabolic markers and distinct patterns of global and regional brain morphology. The generated atlas of associations provides a better understanding of the role of metabolic pathways in structural brain development and maintenance, in both health and disease.

The brain, rapid eye movement sleep, and major depressive disorder: A multimodal neuroimaging study

BACKGROUND

Evidence has established the prominent involvement of rapid eye movement (REM) sleep disturbance in major depressive disorder (MDD). However, the neural correlates of REM sleep in MDD and their clinical significance are less clear.

METHODS

Cross-sectional and longitudinal polysomnography and resting-state functional MRI data were collected from 131 MDD patients and 71 healthy controls to measure REM sleep and voxel-mirrored homotopic connectivity (VMHC). Correlation and mediation analyses were performed to examine the associations between REM sleep, VMHC, and clinical variables. Moreover, we conducted spatial correlations between the neural correlates of REM sleep and a multimodal collection of reference brain maps to facilitate genetic, structural and functional annotations.

RESULTS

MDD patients exhibited REM sleep abnormalities manifesting as higher REM sleep latency and lower REM sleep duration, which were correlated with decreased VMHC of the precentral gyrus and inferior parietal lobe and mediated their associations with more severe anxiety symptoms. Longitudinal data showed that VMHC increase of the inferior parietal lobe was related to improvement of depression symptoms in MDD patients. Spatial correlation analyses revealed that the neural correlates of REM sleep in MDD were linked to gene categories primarily involving cellular metabolic process, signal pathway, and ion channel activity as well as linked to cortical microstructure, metabolism, electrophysiology, and cannabinoid receptor.

CONCLUSION

These findings may add important context to the growing literature on the complex interplay between sleep and MDD, and more broadly may inform future treatment for depression via regulating sleep.

Embracing variability in the search for biological mechanisms of psychiatric illness

Despite decades of research, we lack objective diagnostic or prognostic biomarkers of mental health problems. A key reason for this limited progress is a reliance on the traditional case-control paradigm, which assumes that each disorder has a single cause that can be uncovered by comparing average phenotypic values of patient and control samples. Here, we discuss the problematic assumptions on which this paradigm is based and highlight recent efforts that seek to characterize, rather than minimize, the inherent clinical and biological variability that underpins psychiatric populations. Embracing such variability is necessary to understand pathophysiological mechanisms and develop more targeted and effective treatments.

mFusion: a multiscale fusion method bridging neuroimages to genes through neurotransmissions in mental health disorders

Mental health disorders emerge from complex interactions among neurobiological processes across multiple scales, which poses challenges in uncovering pathological pathways from molecular dysfunction to neuroimaging changes. Here, we proposed a multiscale fusion (mFusion) method to evaluate the relevance of each gene to the neuroimaging traits of mental health disorders. We combined gene-neuroimaging associations with gene-positron emission tomography (PET) and PET-neuroimaging associations using protein-protein interaction networks, where various genes traced by PET maps are involved in neurotransmission. Compared with previous methods, the proposed algorithm identified more disease genes on both simulated and empirical data sets. Applying mFusion to eight mental health disorders, we found that these disorders formed three clusters with distinct associated genes. In summary, mFusion is a promising tool of prioritizing genes for mental health disorders by establishing gene-PET-neuroimaging pathways.