Ultra-low-field (ULF) MRI facilitates neuroimaging access, yet its application in early infancy is constrained by low resolution and contrast, and the limited suitability of existing segmentation tools. In this work we introduce and validate miniMORPH, an open-source pipeline for automated brain volumetry from 0.064T T2-weighted MRI acquired across infancy and toddlerhood. ULF scans were acquired from infants aged 2 to 27 months across two cohorts in South Africa and Uganda. Age-specific templates and priors were used to segment major brain tissues and substructures. Validation used two high-field (HF) references: (i) expert manual HF segmentations for key ROIs across ages, and (ii) automated HF segmentations from SuperSynth on paired HF-ULF scans. We quantified (a) between-subject ordering across modalities using Pearson's correlation (r) and (b) systematic scaling differences using percentage error (PE) and time-corrected percentage error (CPE), stratifying performance by cohort and age. Face validity was also tested via mixed-effects models of age, sex, and birthweight. miniMORPH generated anatomically plausible segmentations of major brain regions across infancy. In paired HF-ULF comparisons, between-subject ordering was generally preserved across many ROIs, with stronger correspondence in the South African cohort than in the Ugandan cohort at 12 months. Systematic scaling offsets were most evident in CSF-rich or boundary-sensitive compartments, with consistently negative CPE for ventricles and cerebellum. Performance varied with age, showing the greatest variability at 3 months. miniMORPH successfully captured regional age-related growth trajectories. Sex-dependent volumetric differences were widespread but attenuated after intracranial volume correction. Low birthweight infants exhibited reduced regional volumes and altered growth trajectories. Taken together, these findings indicate that miniMORPH enables volumetric analysis of ULF infant MRI and preserves between-subject variation suitable for developmental and group analyses. ROI- and cohort-specific offsets, particularly in CSF-rich regions, may require calibration when absolute volumes are needed. The pipeline is openly available at https://github.com/UNITY-Physics/fw-minimorph.

Establishing the reliability of spinal cord functional magnetic resonance imaging (fMRI) is critical before employing it to assess experimental or clinical interventions. Previous studies have mapped human motor activity primarily to the ipsilateral ventral horn, aligning with myotomal and dermatomal projections. Despite these insights, the test-retest reliability of spinal fMRI remains under-investigated. Here we assessed spinal cord activation during a sensorimotor paradigm involving right-hand grasping and grip force estimation in 30 healthy volunteers. Participants completed two identical scanning visits, each time performing the same task twice, enabling the investigation of test-retest reliability both within a single experimental visit and between visits performed on different days. Aggregating all task runs, motor-evoked activation was observed in ipsilateral ventro-dorsal regions of spinal segmental levels C5-T1, as well as in medial regions of levels C2-C3. Despite highly reliable task performance (grip force) and fMRI signal quality (temporal signal-to-noise ratio), the reliability of motor activation was predominantly poor -to- fair both within and between visits, with notable variability in spatial distribution observed across task runs. Increasing the number of task runs per individual improved the robustness of group-level activation, as indexed by higher activated voxel count, larger cluster spatial extent, and attenuated t-statistic distribution. Although we demonstrated that motor-evoked activation corresponds to the known neuroanatomical organisation of motor circuits, its low test-retest reliability presents a challenge for wider applications of spinal fMRI. Understanding the drivers of low reliability in functional imaging is warranted, but we suggest that looking beyond measurement error is required, including careful consideration of inherent within-individual variability underpinned by neurophysiological and psychological factors.

Inequitable access to neuroimaging represents a critical barrier to paediatric neurological care across sub-Saharan Africa, where fewer than one MRI scanner exists per million people and many children with serious neurological conditions are diagnosed late or not at all. Portable ultra-low-field magnetic resonance imaging (ULF-MRI) offers a potentially transformative pathway to closing this gap, yet evidence supporting its integration into real-world acute paediatric clinical workflows in low-resource hospitals remains scarce. We evaluated the operational feasibility, workflow integration, diagnostic adequacy, and proof-of-concept clinical utility of a portable 64 mT ULF-MRI system at a referral hospital in southern Malawi with no on-site conventional brain imaging.This pilot single-case observational study was conducted at Zomba Central Hospital. A 9-year-old female with progressive neurological decline and suspected white matter disease underwent brain imaging using triplanar T2-weighted and diffusion-weighted protocols on a Hyperfine Swoop 64 mT system. Workflow processes from referral to consensus diagnosis were prospectively documented alongside assessment of image quality, artefacts, diagnostic adequacy, and radiologist diagnostic confidence. Images were uploaded to a cloud platform for remote radiologist review.The complete workflow was accomplished within 22 hours, without sedation or adverse events. All six planned sequences were acquired, yielding whole-brain coverage with diagnostically adequate image quality (overall confidence: 4/5, T2: 4/5; DWI: 3/5). T2-weighted imaging demonstrated bilateral frontal and parietal white matter hyperintensity with a periventricular rim and supratentorial ventricular dilatation; diffusion-weighted imaging and ADC maps showed no restricted diffusion, supporting a chronic process. Integrated with the clinical history, findings supported a working diagnosis of probable juvenile-onset Alexander disease — a leukodystrophy that would otherwise have required referral to a distant facility or remained undiagnosed.These findings demonstrate that portable ULF-MRI, supported by remote radiologist review, can bridge the neuroimaging access gap in low-resource hospitals, enabling timely, radiation-free characterisation of white matter disease for children who would otherwise have no imaging pathway. Larger-scale studies are needed to confirm reproducibility and inform equitable scale-up across similar settings.

Background:Traditional classifications of antidepressant medications are organized around primary molecular targets or historical development pathways. However, these categorical systems obscure substantial heterogeneity in receptor- and transporter-level pharmacology both within and across classes. Many antidepressants exhibit clinically relevant polypharmacology, engaging multiple neuromodulatory systems at biologically meaningful affinities. Therefore, we undertook a data-driven reappraisal of antidepressant pharmacology based on multidimensional ligand-target binding profiles. Methods:Experimentally measured inhibition constants (Ki) were curated from public pharmacological databases to construct receptor and transporter affinity "fingerprints" for 25 commonly prescribed antidepressants across serotonergic, dopaminergic, histaminergic, cholinergic, and adrenergic targets. Median pKi values were aggregated to generate a drug × target affinity matrix. This matrix was analyzed using unsupervised hierarchical clustering and principal component analysis, without reference to conventional therapeutic class labels. Results:Antidepressants formed a structured yet continuous pharmacological landscape organized along graded dimensions reflecting transporter selectivity versus receptor-level polypharmacology. Drugs traditionally grouped within the same class frequently diverged in their multidimensional affinity profiles, whereas compounds from different classes often clustered together. Histaminergic, muscarinic, and adrenergic targets were major contributors to the separation between broadly acting and more selective agents. Conclusions:Antidepressants are more accurately characterized by multidimensional affinity architectures than by categorical class labels. Affinity-based representations provide a mechanistically transparent framework for linking molecular pharmacology to systems-level brain function and for understanding how antidepressants engage distributed neuromodulatory systems beyond their traditionally defined primary targets.

Partial volume effects (PVEs) bias PET imaging, particularly in neurodegenerative diseases with regional atrophy such as Huntington’s disease (HD). Despite their relevance, partial volume correction (PVC) methods remain inconsistently applied in HD PET studies. This study compared three MRI-assisted voxelwise PVC approaches to identify the most robust method for quantifying phosphodiesterase 10A availability using 11 C-IMA107 PET in manifest HD. 11 C-IMA107 PET and T1-weighted MRI data were analyzed from 10 people with manifest HD and 10 age- and sex-matched healthy controls (HCs). Parametric binding potential (BP ND ) maps were generated and corrected using three voxelwise MRI-assisted PVC methods: Multiresolution-Multimodal Resolution-Recovery (MMRR), Van Cittert (VC), and Zhu’s deconvolution-based approach. Regional BP ND values were extracted from basal ganglia regions. Group comparisons, correlations between binding and regional volumes, and effect sizes were assessed before and after PVC. Reliability between baseline and 1-year follow-up was evaluated in HCs using intraclass correlation coefficients (ICC). All PVC methods increased BP ND relative to uncorrected estimates, with larger effects in HD, consistent with stronger PVEs due to atrophy. Group differences between HD and HCs were preserved and enhanced after PVC, particularly in striatal regions. VC and Zhu produced highly consistent quantitative results, while MMRR showed greater variability. Zhu’s method provided the best balance between contrast recovery, noise control, and longitudinal reliability (ICC>0.82 in striatal regions). Voxelwise PVC improves sensitivity and reliability of 11 C-IMA107 PET quantification in HD. Among evaluated approaches, Zhu showed the most favorable balance between stability and sensitivity, supporting its use in longitudinal and interventional HD PET studies.

Researchers are increasingly studying cognitive and psychological constructs using automated online tools due to advantages in scalability, repeatability, accessibility and affordability. The online assessment of sleep presents a challenge, as the most popular instruments for reporting different aspects of sleep were originally designed to be deployed under supervised conditions by trained personnel. Here, we develop and validate the Comprehensive Online Sleep Monitoring Scale (COSMOS), a self-reported sleep scale optimised for online, independent administration. Using data from N = 5,815 adults, we show that COSMOS has good internal (Cronbach’s α = 0.85) and convergent validity, via strong associations with established sleep scales. Poorer COSMOS sleep scores were found in participants diagnosed with mental health conditions, more frequent depression or anxiety symptoms, and higher compulsivity or neuroticism traits, demonstrating good construct validity. We propose COSMOS as a comprehensive and validated sleep assessment that is suitable for large-scale online transdiagnostic and mental health research.

Diffusion time-dependence, defined as variations in diffusivity and/or diffusional kurtosis with diffusion time, has emerged as a valuable non-invasive imaging marker for characterizing tissue microstructural features, such as cell size, density, packing disorder, and membrane permeability. In white matter, diffusion time-dependent changes between the short diffusion time and long diffusion time in radial diffusivity (RD), defined as the diffusivity perpendicular to fiber tracts, were demonstrated to correlate strongly with mean axon diameter in ex vivo spinal cord tissues, and to reveal demyelination in mouse corpus callosum. Despite their potential to non-invasively unveil neuronal microstructures to improve the assessment and targeted therapy of neurological diseases, these novel image contrasts obtained at short diffusion times using oscillating gradient spin echo (OGSE) have only recently become feasible for human in vivo studies with high-performance gradient MRI systems. In this preliminary study, we characterized time-dependent RD with OGSE encoding in the human brain in vivo. The change in radial diffusivity between short diffusion time and long diffusion time (ΔRD) consistently exhibited high values in the corticospinal tract, indicating high sensitivity of ΔRD to large axon diameter in human brains. Imaging at a high OGSE frequency of 100 Hz and a moderate b-value of 800 s/mm2 produced the highest ΔRD in the corticospinal tract. This study established a baseline for future investigations of neuronal microstructural alterations in neurological disorders and diseases.

Brain magnetic resonance imaging (MRI) is essential for diagnosis and neurodevelopmental research, but the high cost and infrastructure demands of high-field MRI limit its use to high-income settings. Ultra-low-field MRI scanners offer a more affordable and energy-efficient alternative, but their reduced resolution and signal-to-noise ratio restrict research and clinical utility, prompting the need for super-resolution techniques. Current super-resolution methods rely on either three anisotropic ultra-low-field scans acquired at different orientations (axial, coronal, sagittal) to reconstruct a higher-resolution image using multi-resolution registration (MRR) or the training of deep learning models using paired ultra-low- and high-field scans. Since acquiring three high-quality ultra-low-field scans is not always feasible, and paired high-field data may not be available, this study explores the efficacy of using a deep learning model to generate scans of MRR quality from a single ultra-low-field input scan. Results demonstrated significant enhancement in the quality of output scans, including improved image quality metrics, stronger tissue volume correlations, and greater Dice overlap of tissue segmentations. Generating higher-resolution brain scans from single ultra-low-field scans, without paired high-field data, reduces scanning time and further widens MRI accessibility in low- and middle-income countries. This approach also facilitates site-specific model training, which an exploratory external validation suggests may be necessary to address potential domain shifts across scanning sites.

OBJECTIVE:Preclinical evidence suggests that modulating neural excitation through administration of diazepam, a positive allosteric modulator of GABAA receptors, can prevent the emergence of behavioral and neurobiological alterations relevant to psychosis in adulthood. DESIGN AND PARTICIPANTS:Here, we examine this neurochemical mechanism in individuals at clinical high risk for psychosis in a randomized, double-blind, placebo-controlled crossover study. Twenty-four individuals (15 female and 9 male) aged 18-35 were scanned twice using proton magnetic resonance spectroscopy to measure anterior cingulate cortex Glx (glutamate and glutamine) levels, once after a single dose of diazepam (5 mg) and once after placebo. RESULTS:Mixed-effects model analyses revealed that diazepam reduced anterior cingulate cortex Glx levels compared to placebo (t(20.8) = -2.14, P = .04). The effect of diazepam on Glx levels was greater in older individuals at clinical high risk for psychosis (t(12) = -4.36, P = .001). CONCLUSION:These findings suggest that pharmacological modulation of GABAA receptors can alter Glx changes in and support a novel therapeutic mechanism of benefit for individuals at clinical high risk of psychosis.

[This corrects the article DOI: 10.1162/IMAG.a.930.].

Abstract Background Children in low- and middle-income countries (LMICs) face an elevated risk of developmental delay, yet scalable neuroimaging tools to study early brain development in these contexts remain limited. Children who are HIV-exposed but uninfected (CHEU) represent a growing population with evidence of language and motor delays and altered brain development compared with children who are HIV-unexposed (CHU). Ultra-low-field (ULF) MRI offers a more affordable alternative to conventional high-field (HF) MRI, but its application in early childhood remains underexplored. Methods We compared brain volumes derived from ULF (64mT) and HF (3T) MRI in South African CHEU and CHU as part of the DolPHIN-2 PLUS study. Volumetric segmentation was performed using FreeSurfer v7.4.1 and SynthSeg on the Flywheel platform. Agreement between modalities was assessed using Pearson’s and Lin’s concordance correlation coefficients across global and subcortical regions. Associations between ULF-derived brain volumes and developmental outcomes, measured by the Bayley Scales of Infant Development, Third Edition, were evaluated using partial correlations adjusted for sex and age. Results Forty-five children (9 CHEU, 36 CHU; mean age 45.6 months) had paired ULF and HF scans of usable quality. Strong correlations were observed between ULF and HF volumes for global white and grey matter regions (r > 0.92) and larger subcortical grey matter structures such as the thalamus, caudate, and putamen (r = 0.86–0.89). Moderate-to-weak correlations were evident in smaller structures (hippocampus, pallidum, amygdala). ULF underestimated most grey matter volumes, and overestimated total white matter volume relative to HF. ULF-derived global and subcortical volumes were associated with receptive and expressive communication (r = 0.34–0.59, all p < 0.05). Conclusions ULF MRI produces brain volume estimates comparable to HF MRI and captures meaningful associations with early language development. These findings support ULF MRI as a feasible and scalable tool for studying neurodevelopment in vulnerable paediatric populations in LMICs.

Partial volume effects (PVEs) bias PET imaging, particularly in neurodegenerative diseases with regional atrophy such as Huntington’s disease (HD). Despite their relevance, partial volume correction (PVC) methods remain inconsistently applied in HD PET studies. This study compared three MRI-assisted voxelwise PVC approaches to identify the most robust method for quantifying phosphodiesterase 10A availability using 11C-IMA107 PET in manifest HD.11C-IMA107 PET and T1-weighted MRI data were analyzed from 10 people with manifest HD and 10 age- and sex-matched healthy controls (HCs). Parametric binding potential (BPND) maps were generated and corrected using three voxelwise MRI-assisted PVC methods: Multiresolution-Multimodal Resolution-Recovery (MMRR), Van-Cittert (VC), and Zhu’s deconvolution-based approach. Regional BPND values were extracted from basal ganglia regions. Group comparisons, correlations between binding and regional volumes, and effect sizes were assessed before and after PVC. Reliability between baseline and 1-year follow-up was evaluated in HCs using intraclass correlation coefficients (ICC).All PVC methods increased BPND relative to uncorrected estimates, with larger effects in HD, consistent with stronger PVEs due to atrophy. Group differences between HD and HCs were preserved and enhanced after PVC, particularly in striatal regions. VC and Zhu produced highly consistent quantitative results, while MMRR showed greater variability. Zhu’s method provided the best balance between contrast recovery, noise control, and longitudinal reliability (ICC>0.82 in striatal regions).Voxelwise PVC improves the sensitivity and reliability of 11C-IMA107 PET quantification in HD. Among evaluated approaches, Zhu showed the most favorable balance between stability and sensitivity, supporting its use in longitudinal and interventional HD PET studies.

Post-COVID-19 syndrome encompasses persistent cognitive, neurological, and psychiatric symptoms following SARS-CoV-2 infection, profoundly affecting global quality of life. Clarifying the neurobiological basis of these symptoms is vital for effective therapeutic interventions. This study utilized normative modelling of brain structure ("CentileBrain") to quantify subject-level deviations in cortical thickness, surface area, and subcortical volumes among 20 patients experiencing persistent fatigue following mild COVID-19, compared to 20 matched healthy controls. Group-level analyses on deviation scores revealed subtle yet distinct regional alterations in cortical thickness, specifically decreased thickness within orbitofrontal cortices and increased thickness in occipital/sensory cortices. Although at the individual regional level, the proportion of patients exhibiting infranormal or supranormal thickness values was relatively low (<35%) and comparable to controls, deviations frequently clustered within structurally connected circuits, affecting up to 50% more of patients. Spatial analysis of regional cortical thickness alterations correlated significantly with the constitutive expression patterns of TMPRSS2, an essential protein facilitating SARS-CoV-2 cellular entry. Canonical correlation analyses further identified specific cell-type distributions and neuroreceptor densities predictive of regional thickness changes, highlighting neurons and molecular targets associated with serotoninergic, cannabinoid, cholinergic, and glutamatergic signalling pathways. Network-diffusion modelling constrained by a canonical structural connectome significantly outperformed null models based on permuted connectomes and Euclidean distance metrics, identifying posterior-parietal regions as probable initiation points ("seeds") for network-wide structural changes. Seed likelihood correlated positively with TMPRSS2 expression levels, suggesting that these posterior-parietal regions may be particularly susceptible to SARS-CoV-2 infection. This highlights a plausible mechanism where structural alterations could propagate through connected neural networks, although direct evidence of such propagation requires further investigation. These findings provide novel insights into potential mechanisms underlying neural circuit disruptions in post-COVID-19 fatigue and suggest avenues for therapeutic neuromodulation.

Ultra-low-field (ULF) MRI is emerging as an alternative modality to high-field (HF) MRI due to its lower cost, minimal siting requirements, portability, and enhanced accessibility factors that enable large-scale deployment. Although ULF-MRI exhibits lower signal-to-noise ratio (SNR), advanced imaging and data-driven denoising methods enabled by high-performance computing have made contrasts like diffusion-weighted imaging (DWI) feasible at ULF. This study investigates the potential and limitations of ULF tractography, using data acquired on a 0.064 T commercially available mobile point-of-care MRI scanner. The results demonstrate that most major white matter bundles can be successfully retrieved in healthy adult brains within clinically tolerable scan times. This study also examines the recovery of diffusion tensor imaging (DTI)-derived scalar maps, including fractional anisotropy and mean diffusivity. Strong correspondence is observed between scalar maps obtained with ULF-MRI and those acquired at high field strengths. Furthermore, fibre orientation distribution functions reconstructed from ULF data show good agreement with high-field references, supporting the feasibility of using ULF-MRI for reliable tractography. These findings open new opportunities to use ULF-MRI in studies of brain health, development, and disease progression particularly in populations traditionally underserved due to geographic or economic constraints. The results show that robust assessments of white matter microstructure can be achieved with ULF-MRI, effectively democratising microstructural MRI and extending advanced imaging capabilities to a broader range of research and clinical settings where resources are typically limited.

Histamine is a key neuromodulator shaping cognition, emotion and behavioral flexibility, yet its organization in the human brain remains incompletely characterized. We conducted a multimodal analysis integrating transcriptomic, neuroimaging, developmental and functional datasets to map the architecture of the histaminergic system. At the single-cell level, histamine receptor H-1 and histamine receptor H-2 were enriched in excitatory neurons, whereas histamine receptor H-3 showed preferential expression in inhibitory populations. Regional expression of core histaminergic genes was captured by a single latent component (41.1% of variance), with higher expression in frontal and limbic regions and lower expression in the occipital cortex. This spatial signature predicted in vivo H-3 receptor binding across independent positron emission tomography datasets. Functional decoding linked histaminergic expression to emotion regulation, salience processing, impulsivity, sleep, memory and reward. Developmentally, histidine decarboxylase expression peaked early, whereas histamine receptor H-3 increased into adulthood. Finally, histaminergic expression correlated with structural alteration patterns in attention deficit hyperactivity disorder, major depressive disorder, schizophrenia and anorexia nervosa, suggesting relevance for regional vulnerability in psychiatric disorders.

Background:Post-COVID-19 Syndrome (PCS) is characterised by persistent fatigue, cognitive impairments, and affective symptoms, yet its underlying neural mechanisms remain poorly understood. While static neuroimaging studies have identified resting-state connectivity abnormalities in PCS, such approaches fail to capture the brain's dynamic functional organisation. This represents a missed opportunity to understand how alterations in large-scale network interactions may contribute to the fluctuating symptom profile of PCS. Cognitive and emotional processes rely on the brain's capacity to flexibly reconfigure large-scale networks over time; disruptions in this dynamic repertoire may therefore play a role in PCS pathophysiology. Methods:Resting-state fMRI data were acquired from 20 individuals with PCS (mean age = 41.8 years, SD = 9.4) and 20 age- and sex-matched healthy controls (mean age = 40.6 years, SD = 8.1) using a multi-echo sequence. Following denoising with multi-echo independent component analysis, we applied Leading Eigenvector Dynamics Analysis (LEiDA) to identify recurrent patterns of whole-brain phase synchrony. The optimal number of dynamic brain states was determined using the Dunn index. For each state, we quantified probability of occurrence, lifetime, and transition probabilities, and mapped spatial topographies onto canonical functional networks. Group differences were assessed using ANCOVAs controlling for age, sex, and handedness. Exploratory associations with clinical symptoms, cognitive performance, and inflammatory markers were examined using both frequentist and Bayesian approaches. Results:Five recurrent dynamic brain states were identified. Compared with controls, PCS participants showed reduced probability of occurrence and shorter lifetime of a visual/dorsal attention state, alongside increased probability of a limbic/default mode network (DMN) state. PCS was also characterised by tentative reduced transitions between visual/dorsal attention and frontoparietal-DMN states, and increased transitions from somatomotor/visual states toward the limbic-DMN configuration. Exploratory analyses (uncorrected for multiple comparisons) suggested that greater expression of the limbic-DMN state was associated with lower global cognitive performance (MoCA) and higher serum IL-1β levels, although these associations did not survive correction for multiple comparisons. Conclusions:PCS is associated with a reorganisation of intrinsic brain dynamics, marked by a shift from externally oriented attentional states toward limbic-DMN configurations and reduced transition flexibility. These findings suggest that PCS may involve alterations in the dynamic balance of large-scale brain systems supporting attention and internally oriented processing. While exploratory, the observed patterns are consistent with a potential link between brain-state dynamics, cognitive function, and inflammatory signalling, and provide a systems-level framework for future studies of post-viral brain dysfunction.

Magnetic resonance imaging (MRI) is critical for neurodevelopmental research, however access to high-field (HF) systems in low- and middle-income countries is severely hindered by their cost. Ultra-low-field (ULF) systems mitigate such issues of access inequality, however their diminished signal-to-noise ratio limits their applicability for research and clinical use. Deep-learning approaches can enhance the quality of scans acquired at lower field strengths at no additional cost. For example, Convolutional neural networks (CNNs) fused with transformer modules have demonstrated a remarkable ability to capture both local information and long-range context. Unfortunately, the quadratic complexity of transformers leads to an undesirable trade-off between long-range sensitivity and local precision. We propose a hybrid CNN and state-space model (SSM) architecture featuring a novel 3D to 1D serialisation (GAMBAS), which learns long-range context without sacrificing spatial precision. We exhibit improved performance compared to other state-of-the-art medical image-to-image translation models.

BACKGROUND:Cannabis constituents, including Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD), show distinct pharmacological profiles with therapeutic relevance for neurological and psychiatric conditions. THC exerts euphoric effects primarily via CB1 receptor activation, while CBD displays non-euphoric properties affecting various pathways. AIMS:This study evaluated the effects of THC, CBD, and their combination on brain functional connectivity (FC) and cerebral blood flow (CBF) using multimodal neuroimaging. METHODS:Adult male Sprague Dawley rats received intraperitoneal doses of 10 mg/kg THC, 150 mg/kg CBD, 10.8:10 mg/kg THC:CBD, or vehicle. Resting-state blood oxygenation level dependent magnetic resonance imaging and arterial spin labelling assessed FC and CBF, approximately 2 h after drug administration. Graph-theory metrics and seed-based analyses identified connectivity and perfusion alterations, while plasma analyses determined cannabinoid concentrations. RESULTS:THC increased whole-brain FC and clustering coefficient, with elevated CBF in cortical and subcortical regions. CBD decreased FC metrics without affecting CBF, while THC:CBD induced moderate increases in both. Seed-based analysis revealed THC-driven increases in cortical-hippocampal and cortical-striatal connectivity, attenuated in the THC:CBD group. A multivariate combined analysis of FC and CBF revealed a divergent pattern of changes induced by each drug. CONCLUSIONS:In conclusion, we show that THC and CBD induce distinct neurophysiological profiles in rats, with THC increasing both connectivity and perfusion, moderated by CBD when combined. These findings corroborate existing knowledge about the effects of cannabinoids on the brain, while also supporting the potential of preclinical functional neuroimaging to delineate cannabinoid-induced endophenotypes, offering insights for therapeutic development.

Abstract Background This study aimed to determine whether associations of antenatal maternal anaemia with smaller corpus callosum, caudate nucleus, and putamen volumes previously described in children at age 2–3 years persisted to age 6–7 years in the Drakenstein Child Health Study (DCHS). Methods This neuroimaging sub-study was nested within the DCHS, a South African population-based birth cohort. Pregnant women were enrolled (2012–2015) and mother–child dyads were followed prospectively. A sub-group of children had magnetic resonance imaging at 6–7 years of age (2018–2022). Mothers had haemoglobin measurements during pregnancy and a proportion of children were tested postnatally. Maternal anaemia (haemoglobin < 11 g/dL) and child anaemia were classified using WHO and local guidelines. Linear modeling was used to investigate associations between antenatal maternal anaemia status, maternal haemoglobin concentrations, and regional child brain volumes. Models included potential confounders and were conducted with and without child anaemia to assess the relative roles of antenatal versus postnatal anaemia. Results Overall, 157 children (Mean [SD] age of 75.54 [4.77] months; 84 [53.50%] male) were born to mothers with antenatal haemoglobin data. The prevalence of maternal anaemia during pregnancy was 31.85% (50/157). In adjusted models, maternal anaemia status was associated with smaller volumes of the total corpus callosum (adjusted percentage difference, − 6.77%; p = 0.003), left caudate nucleus (adjusted percentage difference, − 5.98%, p = 0.005), and right caudate nucleus (adjusted percentage difference, − 6.12%; p = 0.003). Continuous maternal haemoglobin was positively associated with total corpus callosum (β = 0.239 [CI 0.10 to 0.38]; p < 0.001) and caudate nucleus (β = 0.165 [CI 0.02 to 0.31]; p = 0.027) volumes. In a sub-group (n = 89) with child haemoglobin data (Mean [SD] age of 76.06 [4.84]), the prevalence of antenatal maternal anaemia and postnatal child anaemia was 38.20% (34/89) and 47.19% (42/89), respectively. There was no association between maternal and child anaemia (χ 2 = 0.799; p = 0.372), and child anaemia did not contribute to regional brain volume differences associated with maternal anaemia. Conclusions Associations between maternal anaemia and regional child brain volumes previously reported at 2–3 years of age were consistent and persisted to 6–7 years of age. Findings support the importance of optimising antenatal maternal health and reinforce these brain regions as a future research focus.