The associative striatum, an established substrate in psychosis, receives widespread glutamatergic projections. We sought to see if glutamatergic indices are altered between early psychosis patients with and without a history of cannabis use and characterise the relationship to grey matter. 92 participants were scanned: Early Psychosis with a history of cannabis use (EPC = 29); Early Psychosis with minimal cannabis use (EPMC = 25); Controls with a history of cannabis use (HCC = 16) and Controls with minimal use (HCMC = 22). Whole brain T1 weighted MR images and localised proton MR spectra were acquired from head of caudate, anterior cingulate and hippocampus. We examined relationships in regions with known high cannabinoid 1 receptor (CB1R) expression (grey matter, cortex, hippocampus, amygdala) and low expression (white matter, ventricles, brainstem) to caudate Glutamine+Glutamate (Glx). Patients were well matched in symptoms, function and medication. There was no significant group difference in Glx in any region. In EPC grey matter volume explained 31.9% of the variance of caudate Glx (p = 0.003) and amygdala volume explained 36.9% (p = 0.001) of caudate Glx. There was no significant relationship in EPMC. The EPC vs EPMC interaction was significant (p = 0.042). There was no such relationship in control regions. These results are the first to demonstrate association of grey matter volume and striatal glutamate in the EPC group. This may suggest a history of cannabis use leads to a conformational change in distal CB1 rich grey matter regions to influence striatal glutamatergic levels or that such connectivity predisposes to heavy cannabis use.

We aimed to investigate changes in regional cerebral blood flow (rCBF) using arterial spin labelling (ASL) in patients with visual snow syndrome (VSS), in order to understand more about the underlying neurobiology of the condition, which remains mostly unknown.

Pattern recognition approaches, such as the Support Vector Machine (SVM), have been successfully used to classify groups of individuals based on their patterns of brain activity or structure. However these approaches focus on finding group differences and are not applicable to situations where one is interested in accessing deviations from a specific class or population. In the present work we propose an application of the one-class SVM (OC-SVM) to investigate if patterns of fMRI response to sad facial expressions in depressed patients would be classified as outliers in relation to patterns of healthy control subjects. We defined features based on whole brain voxels and anatomical regions. In both cases we found a significant correlation between the OC-SVM predictions and the patients' Hamilton Rating Scale for Depression (HRSD), i.e. the more depressed the patients were the more of an outlier they were. In addition the OC-SVM split the patient groups into two subgroups whose membership was associated with future response to treatment. When applied to region-based features the OC-SVM classified 52% of patients as outliers. However among the patients classified as outliers 70% did not respond to treatment and among those classified as non-outliers 89% responded to treatment. In addition 89% of the healthy controls were classified as non-outliers.

Psychotic disorders are associated with widespread reductions in white matter (WM) integrity. However, the stage at which these abnormalities first appear and whether they are correlates of psychotic illness, as opposed to an increased vulnerability to psychosis, is unclear. We addressed these issues by using diffusion tensor imaging (DTI) to study subjects at ultra high risk (UHR) of psychosis before and after the onset of illness.

It is unclear whether early psychosis in the context of cannabis use is different from psychosis without cannabis. We investigated this issue by examining whether abnormalities in oculomotor control differ between patients with psychosis with and without a history of cannabis use. We studied four groups: patients in the early phase of psychosis with a history of cannabis use (EPC; n = 28); patients in the early phase of psychosis without (EPNC; n = 25); controls with a history of cannabis use (HCC; n = 16); and controls without (HCNC; n = 22). We studied smooth pursuit eye movements using a stimulus with sinusoidal waveform at three target frequencies (0.2, 0.4 and 0.6 Hz). Participants also performed 40 antisaccade trials. There were no differences between the EPC and EPNC groups in diagnosis, symptom severity or level of functioning. We found evidence for a cannabis effect (χ2 = 23.14, p < 0.001), patient effect (χ2 = 4.84, p = 0.028) and patient × cannabis effect (χ2 = 4.20, p = 0.04) for smooth pursuit velocity gain. There was a large difference between EPC and EPNC (g = 0.76-0.86) with impairment in the non cannabis using group. We found no significant effect for antisaccade error whereas patients had fewer valid trials compared to controls. These data indicate that impairment of smooth pursuit in psychosis is more severe in patients without a history of cannabis use. This is consistent with the notion that the severity of neurobiological alterations in psychosis is lower in patients whose illness developed in the context of cannabis use.

We propose a novel approach to predicting disease progression in Alzheimer's disease (AD)--multivariate ordinal regression--which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression--the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer's Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ =  -0.64, ADNI and ρ =  -0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12-24 months) and late converters (24-36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.

Resting-state functional MRI (rsfMRI) is an imaging technology that has recently gained attention for its ability to detect disruptions in functional brain networks in humans, including in patients with Parkinson's disease (PD), revealing early and widespread brain network abnormalities. This methodology is now readily applicable to experimental animals offering new possibilities for cross-species translational imaging. In this context, we herein describe the application of rsfMRI to the unilaterally-lesioned 6-hydroxydopamine (6-OHDA) rat, a robust experimental model of the dopamine depletion implicated in PD. Using graph theory to analyse the rsfMRI data, we were able to provide meaningful and translatable measures of integrity, influence and segregation of the underlying functional brain architecture. Specifically, we confirm that rats share a similar functional brain network topology as observed in humans, characterised by small-worldness and modularity. Interestingly, we observed significantly reduced functional connectivity in the 6-OHDA rats, primarily in the ipsilateral (lesioned) hemisphere as evidenced by significantly lower node degree, local efficiency and clustering coefficient in the motor, orbital and sensorimotor cortices. In contrast, we found significantly, and bilaterally, increased thalamic functional connectivity in the lesioned rats. The unilateral deficits in the cortex are consistent with the unilateral nature of this model and further support the validity of the rsfMRI technique in rodents. We thereby provide a methodological framework for the investigation of brain networks in other rodent experimental models of PD, as well as of animal models in general, for cross-comparison with human data.

The tremendous clinical and aetiological diversity among individuals with autism spectrum disorder (ASD) has been a major obstacle to the development of new treatments, as many may only be effective in particular subgroups. Precision medicine approaches aim to overcome this challenge by combining pathophysiologically based treatments with stratification biomarkers that predict which treatment may be most beneficial for particular individuals. However, so far, we have no single validated stratification biomarker for ASD. This may be due to the fact that most research studies primarily have focused on the identification of mean case-control differences, rather than within-group variability, and included small samples that were underpowered for stratification approaches. The EU-AIMS Longitudinal European Autism Project (LEAP) is to date the largest multi-centre, multi-disciplinary observational study worldwide that aims to identify and validate stratification biomarkers for ASD.

Advances in neonatal medicine have resulted in a larger proportion of preterm-born individuals reaching adulthood. Their increased liability to psychiatric illness and impairments of cognition and behaviour intimate lasting cerebral consequences; however, the central physiological disturbances remain unclear. Of fundamental importance to efficient brain function is the coordination and contextually-relevant recruitment of neural networks. Large-scale distributed networks emerge perinatally and increase in hierarchical complexity through development. Preterm-born individuals exhibit systematic reductions in correlation strength within these networks during infancy. Here, we investigate resting-state functional connectivity in functional magnetic resonance imaging data from 29 very-preterm (VPT)-born adults and 23 term-born controls. Neurocognitive networks were identified with spatial independent component analysis conducted using the Infomax algorithm and employing Icasso procedures to enhance component robustness. Network spatial focus and spectral power were not generally significantly affected by preterm birth. By contrast, Granger-causality analysis of the time courses of network activity revealed widespread reductions in between-network connectivity in the preterm group, particularly along paths including salience-network features. The potential clinical relevance of these Granger-causal measurements was suggested by linear discriminant analysis of topological representations of connection strength, which classified individuals by group with a maximal accuracy of 86%. Functional connections from the striatal salience network to the posterior default mode network informed this classification most powerfully. In the VPT-born group it was additionally found that perinatal factors significantly moderated the relationship between executive function (which was reduced in the VPT-born as compared with the term-born group) and generalised partial directed coherence. Together these findings show that resting-state functional connectivity of preterm-born individuals remains compromised in adulthood; and present consistent evidence that the striatal salience network is preferentially affected. Therapeutic practices directed at strengthening within-network cohesion and fine-tuning between-network inter-relations may have the potential to mitigate the cognitive, behavioural and psychiatric repercussions of preterm birth.

The posteromedial cortex (PMC) is a key region involved in the development and progression of Alzheimer's disease (AD). Previous studies have demonstrated a heterogenous functional architecture of the region that is composed of discrete functional modules reflecting a complex pattern of functional connectivity. However, little is understood about the mechanisms underpinning this complex network architecture in neurodegenerative disease, and the differential vulnerability of connectivity-based subdivisions in the PMC to AD pathogenesis. Using a data-driven approach, we applied a constrained independent component analysis (ICA) on healthy adults from the Human Connectome Project to characterise the local functional connectivity patterns within the PMC, and its unique whole-brain functional connectivity. These distinct connectivity profiles were subsequently quantified in the Alzheimer's Disease Neuroimaging Initiative study, to examine functional connectivity differences in AD patients and cognitively normal (CN) participants, as well as the entire AD pathological spectrum. Our findings revealed decreased functional connectivity in the anterior precuneus, dorsal posterior cingulate cortex (PCC), and the central precuneus in AD patients compared to CN participants. Functional abnormalities in the dorsal PCC and central precuneus were also related to amyloid burden and volumetric hippocampal loss. Across the entire AD spectrum, functional connectivity of the central precuneus was associated with disease severity and specific deficits in memory and executive function. These findings provide new evidence showing that the PMC is selectively impacted in AD, with prominent network failures of the dorsal PCC and central precuneus underpinning the neurodegenerative and cognitive dysfunctions associated with the disease.

Disruptive behavior disorders (DBD) are heterogeneous at the clinical and the biological level. Therefore, the aims were to dissect the heterogeneous neurodevelopmental deviations of the affective brain circuitry and provide an integration of these differences across modalities.

Myelin is a critical component of the nervous system and a major contributor to contrast in Magnetic Resonance (MR) images. However, the precise contribution of myelination to multiple MR modalities is still under debate. The cuprizone mouse is a well-established model of demyelination that has been used in several MR studies, but these have often imaged only a single slice and analysed a small region of interest in the corpus callosum. We imaged and analyzed the whole brain of the cuprizone mouse ex-vivo using high-resolution quantitative MR methods (multi-component relaxometry, Diffusion Tensor Imaging (DTI) and morphometry) and found changes in multiple regions, including the corpus callosum, cerebellum, thalamus and hippocampus. The presence of inflammation, confirmed with histology, presents difficulties in isolating the sensitivity and specificity of these MR methods to demyelination using this model.

An increasing number of neuroimaging studies are based on either combining more than one data modality (inter-modal) or combining more than one measurement from the same modality (intra-modal). To date, most intra-modal studies using multivariate statistics have focused on differences between datasets, for instance relying on classifiers to differentiate between effects in the data. However, to fully characterize these effects, multivariate methods able to measure similarities between datasets are needed. One classical technique for estimating the relationship between two datasets is canonical correlation analysis (CCA). However, in the context of high-dimensional data the application of CCA is extremely challenging. A recent extension of CCA, sparse CCA (SCCA), overcomes this limitation, by regularizing the model parameters while yielding a sparse solution. In this work, we modify SCCA with the aim of facilitating its application to high-dimensional neuroimaging data and finding meaningful multivariate image-to-image correspondences in intra-modal studies. In particular, we show how the optimal subset of variables can be estimated independently and we look at the information encoded in more than one set of SCCA transformations. We illustrate our framework using Arterial Spin Labeling data to investigate multivariate similarities between the effects of two antipsychotic drugs on cerebral blood flow.

Arterial spin labeling (ASL) provides absolute quantification of resting tissue cerebral blood flow (CBF) as an entirely noninvasive approach with good reproducibility. As a result of neurovascular coupling, ASL provides a useful marker of resting neuronal activity. Recent ASL studies in individuals at clinical high risk of psychosis (CHR) have reported increased resting hippocampal perfusion compared with healthy controls. Schizotypy refers to the presence of subclinical psychotic-like experiences in healthy individuals and represents a robust framework to study neurobiological mechanisms involved in the extended psychosis phenotype while avoiding potentially confounding effects of antipsychotic medications or disease comorbidity. Here we applied pseudo-continuous ASL to examine differences in resting CBF in 21 subjects with high positive schizotypy (HS) relative to 22 subjects with low positive schizotypy (LS), as determined by the Oxford and Liverpool Inventory of Feelings and Experiences. Based on preclinical evidence that hippocampal hyperactivity leads to increased activity in mesostriatal dopamine projections, CBF in hippocampus, midbrain, and striatum was assessed. Participants with HS showed higher CBF of the right hippocampus compared to those with LS (p = .031, family-wise error corrected). No differences were detected in the striatum or midbrain. The association between increased hippocampal CBF and HS supports the notion that hippocampal hyperactivity might be a central characteristic of the extended psychosis phenotype, while hyperactivity in subcortical dopamine pathways may only emerge at a higher intensity of psychotic experiences.

Functional neuroimaging techniques have provided great insight in the field of pain. Utilising these techniques, we have characterised pain-induced responses in the brain and improved our understanding of key pain-related phenomena. Despite the utility of these methods, there remains a need to assess the test retest reliability of pain modulated blood-oxygen-level-dependant (BOLD) MR signal across repeated sessions. This is especially the case for more novel yet increasingly implemented stimulation modalities, such as noxious pressure, and it is acutely important for multi-session studies considering treatment efficacy. In the present investigation, BOLD signal responses were estimated for noxious-pressure stimulation in a group of healthy participants, across two separate sessions. Test retest reliability of functional magnetic resonance imaging (fMRI) data and self-reported visual analogue scale measures were determined by the intra-class correlation coefficient. High levels of reliability were observed in several key brain regions known to underpin the pain experience, including in the thalamus, insula, somatosensory cortices, and inferior frontal regions, alongside "excellent" reliability of self-reported pain measures. These data demonstrate that BOLD-fMRI derived signals are a valuable tool for quantifying noxious responses pertaining to pressure stimulation. We further recommend the implementation of pressure as a stimulation modality in experimental applications.

Cluster headache is an excruciating disorder with no cure. Greater occipital nerve blockades can transiently suppress attacks in approximately 50% of patients, however, its mechanism of action remains uncertain, and there are no reliable predictors of treatment response. To address this, we investigated the effect of occipital nerve blockade on regional cerebral blood flow (rCBF), an index of brain activity, and differences between treatment responders and non-responders. Finally, we compared baseline perfusion maps from patients to a matched group of healthy controls.

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

Genetic factors account for up to 80% of the liability for schizophrenia (SCZ) and bipolar disorder (BD). Genome-wide association studies have successfully identified several genes associated with increased risk for both disorders. This has allowed researchers to model the aggregate effect of genes associated with disease status and create a polygenic risk score (PGRS) for each individual. The interest in imaging genetics using PGRS has grown in recent years, with several studies now published. We have conducted a systematic review to examine the effects of PGRS of SCZ, BD and cross psychiatric disorders on brain function and connectivity using fMRI data. Results indicate that the effect of genetic load for SCZ and BD on brain function affects task-related recruitment, with frontal areas having a more prominent role, independent of task. Additionally, the results suggest that the polygenic architecture of psychotic disorders is not regionally confined but impacts on the task-dependent recruitment of multiple brain regions. Future imaging genetics studies with large samples, especially population studies, would be uniquely informative in mapping the spatial distribution of the genetic risk to psychiatric disorders on brain processes during various cognitive tasks and may lead to the discovery of biological pathways that could be crucial in mediating the link between genetic factors and alterations in brain networks.

The unilaterally-lesioned 6-hydroxydopamine (6-OHDA) rat is one of the most commonly used experimental models of Parkinson's disease (PD). Here we investigated whether magnetic resonance imaging (MRI) that is widely used in human PD research, has the potential to non-invasively detect macroscopic structural brain changes in the 6-OHDA rat in ways translatable to humans.

Obstructive sleep apnea (OSA) is a chronic, multisystem disorder that has a bidirectional relationship with several major neurological disorders, including Alzheimer's dementia. Treatment with Continuous Positive Airway Pressure (CPAP) offers some protection from the effects of OSA, although it is still unclear which populations should be targeted, for how long, and what the effects of treatment are on different organ systems. We investigated whether cognitive improvements can be achieved as early as one month into CPAP treatment in patients with OSA.