Ottavia Dipasquale

The human brain is organized into interacting functional systems. Their underlying neurobiological mechanisms remain difficult to study in vivo ^1,2 . Here, we adopt a topological framework to quantify the association between neurobiology and brain functional connectivity derived from both resting-state functional magnetic resonance imaging (rsfMRI) and magnetic encephalography (MEG). Across six healthy adult cohorts (n = 19–112), regional variation in rsfMRI connectivity robustly aligns with the distribution of neurotransmitter receptors and transporters. We find that low-frequency functional synchronization measured by rsfMRI is predominantly modulated by decreased regional availability of multiple receptors and transporters. These patterns are present in every single subject, replicate across all cohorts, and are mirrored in MEG, where high-frequency synchronization increases with availability of the same receptors and transporters. Most prominently, we observe noradrenergic modulation of functional connectivity in a sensorimotor-posterior-insular network that is consistently detected across individuals and is linked to autonomic arousal. In pharmacological and clinical samples, the associations are sensitive to manipulation of the respective neurotransmitter systems and are altered in patients with early psychosis, aligning with clinical symptomatology. These findings provide biological insight into typical and atypical functional organization of the human brain using a framework linking underlying neurobiology to the functional connectome (NEOFC).

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 at the dynamic macroscale interactions give rise to the complex and 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 the exploration and transition between brain states may therefore play a central 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 reduced transitions between visual/dorsal attention and frontoparietal–DMN states, and increased transitions from somatomotor/visual states toward the limbic-DMN configuration. Exploratory analyses revealed that greater expression of the limbic-DMN state was negatively associated with global cognitive performance (MoCA) and positively associated with serum IL-1β levels. Conclusions: PCS is associated with a reorganisation of intrinsic brain dynamics, marked by a shift from externally oriented attentional states toward limbic-DMN configurations. This imbalance may reflect reduced cognitive flexibility and increased sensitivity to interoceptive or affective signals. The observed links between dynamic brain-state expression, cognitive impairment, and peripheral inflammation support a systems-level mechanism through which immune signalling may influence neural function in PCS. Dynamic functional connectivity offers a sensitive framework for capturing these alterations and may inform future mechanistic models of post-viral dysfunction and therapeutic targeting. Highlights: Post-COVID-19 Syndrome (PCS) is linked to altered intrinsic brain dynamics at rest. Patients show reduced engagement of visual and attentional brain states. Increased recruitment of limbic-DMN states suggests a shift toward more internally, emotionally charged focussed brain activity. Brain-state dynamics are putatively associated with global cognition and IL-1β. Reduced brain-state flexibility may underlie cognitive and emotional symptoms in PCS.

Quantitative neuroimaging techniques have proven to be powerful tools for studying normal brain physiology and investigating the structural and functional alterations associated with different brain disorders. Despite their huge potential, their contribution to the clinic has largely been limited to supporting (when possible) more standardised neuroradiological imaging procedures.Traditionally, quantitative neuroimaging research relies on cross-sectional analyses, where groups of patients sharing similar diagnostic labels are compared to matched groups of controls. However, this approach may overlook important variations among individuals, particularly in the context of disease diagnosis and treatment response.In this respect, normative modelling (NM) represents a promising innovation for the field of neuroimaging, mapping brain imaging anomalies at the individual level, thus facilitating the shift towards personalised applications. Instead of treating all individuals as part of a homogeneous population, NM describes the variance of imaging phenotypes within a reference population and exploits it to identify subject-specific measures of disease states via the quantification of individual deviations from the normative expected model.In this paper, we showcase the latest research on NM in neuroimaging to investigate neurological and neuropsychiatric disorders. We lead the readers through the key steps of implementing the NM framework, starting from the normative model selection to model estimation, evaluation, and application. Finally, we highlight how the NM is applied across various neuroimaging techniques to pinpoint biomarkers of functional and structural brain alterations.

Background: Differentiating brain radionecrosis (RN) from tumor progression (TP) is a persistent clinical difficulty. Here, we compared the diagnostic accuracy of leakage-corrected relative cerebral blood volume (rCBV) and fluid-suppressed amide proton transfer-weighted (APTw) imaging in distinguishing between RN and TP in metastases. Methods: Subjects with enlarging lesions after stereotactic radiosurgery were prospectively examined at 3T. APTw data were acquired with a 3D snapshot-gradient echo sequence. B0 and B1 inhomogeneities were corrected using the WASAB1 protocol. rCBV was calculated according to established guidelines. Image analysis was performed using Olea Sphere 3.0 software. ΔAPTw and ΔrCBV were calculated as the average signal within the lesion normalized against the average signal in the contralateral white matter. A diagnosis of TP or RN was assessed by histology or imaging at follow-up. Independent samples t-tests of ΔAPTw and ΔrCBV and the areas under the curve (AUCs) were computed. Results: Twenty-one metastases (10 RN, 11 TP) were evaluated. APTw differentiated between RN and TP (U = 120, p < 0.001), in contrast to rCBV (U = 71, p = 0.174). The AUC was 0.991 (95% CI = 0.962–1.020) for ΔAPTw, and 0.636 (95% CI = 0.352–0.921) for ΔrCBV. The optimal cutoff points were 0.4 and 2.1 for ΔAPTw and ΔrCBV, respectively. The sensitivity and specificity for RN-TP were 100% and 90% for ΔAPTw and 63.6% and 36.4% for ΔrCBV. Conclusions: Fluid-suppressed APTw metrics enabled more accurate diagnostic performances than leakage-corrected rCBV metrics in distinguishing between RN and TP. These promising results suggest that APTw imaging could valuably complement current multiparametric MRI protocols in brain metastases follow-ups.

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain’s in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility, high costs, and high between-subject heterogeneity of neuroimaging measures. In this study, we explore the use of normative modelling (NM) to identify individual patient alterations by describing the physiological variability of molecular functions. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([¹¹C]-(+)-PHNO and [¹⁸F]FDOPA) to create a reference-cohort model of healthy controls. The models were subsequently utilized on different independent cohorts of patients with psychosis in different disease stages and treatment outcomes. Our results showed that patients with psychosis exhibited a higher degree of extreme deviations (~3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap of extreme deviations topology (max 20%). We also confirmed that striatal [¹⁸F]FDOPA signal, when referenced to a normative distribution, can predict treatment response (striatal AUC ROC: 0.77–0.83). In conclusion, our results indicate that normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. In addition, the method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.

Lysergic acid diethylamide (LSD) is a classic serotonergic psychedelic that induces a profoundly altered conscious state. In conjunction with psychological support, it is currently being explored as a treatment for generalized anxiety disorder and depression. The dorsolateral prefrontal cortex (DLPFC) is a brain region that is known to be involved in mood regulation and disorders; hypofunction in the left DLPFC is associated with depression. This study investigated the role of the DLPFC in the psycho-emotional effects of LSD with functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) data of healthy human participants during the acute LSD experience. In the fMRI data, we measured the correlation between changes in resting-state functional connectivity (RSFC) of the DLPFC and post-scan subjective ratings of positive mood, emotional arousal, and ego dissolution. We found significant, positive correlations between ego dissolution and functional connectivity between the left & right DLPFC, thalamus, and a higher-order visual area, the fusiform face area (FFA). Additionally, emotional arousal was significantly associated with increased connectivity between the right DLPFC, intraparietal sulcus (IPS), and the salience network (SN). A confirmational “reverse” analysis, in which the outputs of the original RSFC analysis were used as input seeds, substantiated the role of the right DLPFC and the aforementioned regions in both ego dissolution and emotional arousal. Subsequently, we measured the effects of LSD on directed functional connectivity in MEG data that was source-localized to the input and output regions of both the original and reverse analyses. The Granger causality (GC) analysis revealed that LSD increased information flow between two nodes of the ‘ego dissolution network’, the thalamus and the DLPFC, in the theta band, substantiating the hypothesis that disruptions in thalamic gating underlie the experience of ego dissolution. Overall, this multimodal study elucidates a role for the DLPFC in LSD-induced states of consciousness and sheds more light on the brain basis of ego dissolution.

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

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all p_FDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all p_FDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all p_FDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

Molecular neuroimaging techniques, like PET and SPECT, offer invaluable insights into the brain’s in-vivo biology and its dysfunction in neuropsychiatric patients. However, the transition of molecular neuroimaging into diagnostics and precision medicine has been limited to a few clinical applications, hindered by issues like practical feasibility and high costs. In this study, we explore the use of normative modelling (NM) for molecular neuroimaging to identify individual patient deviations from a reference cohort of subjects. NM potentially addresses challenges such as small sample sizes and diverse acquisition protocols that are typical of molecular neuroimaging studies. We applied NM to two PET radiotracers targeting the dopaminergic system ([ ¹¹ C]-(+)-PHNO and [ ¹⁸ F]FDOPA) to create a normative model to reference groups of controls. The models were subsequently utilized on various independent cohorts of patients experiencing psychosis. These cohorts were characterized by differing disease stages, treatment responses, and the presence or absence of matched controls. Our results showed that patients exhibited a higher degree of extreme deviations (∼3-fold increase) than controls, although this pattern was heterogeneous, with minimal overlap in extreme deviations topology (max 20%). We also confirmed the value of striatal [ ¹⁸ F]FDOPA signal to predict treatment response (striatal AUC ROC: 0.77-0.83). Methodologically, we highlighted the importance of data harmonization before data aggregation. In conclusion, normative modelling can be effectively applied to molecular neuroimaging after proper harmonization, enabling insights into disease mechanisms and advancing precision medicine. The method is valuable in understanding the heterogeneity of patient populations and can contribute to maximising cost efficiency in studies aimed at comparing cases and controls.