Schizophrenia is believed to be a neurodevelopmental disease with high heritability. Differential diagnosis is often challenging, especially in early phases, namely with other psychotic disorders or even mood disorders. such as bipolar disorder with psychotic symptoms. Key pathophysiological changes separating these two classical psychoses remain poorly understood, and current evidence favors a more dimensional than categorical differentiation between schizophrenia and bipolar disorder. While established biomarkers like cortical thickness and grey matter volume are heavily influenced by post-onset changes and thus provide limited possibility of accessing early pathologies, gyrification is assumed to be more specifically determined by genetic and early developmental factors. The aim of our study was to compare both classical and novel morphometric features in these two archetypal psychiatric disorders. We included 20 schizophrenia patients, 20 bipolar disorder patients and 20 age- and gender-matched healthy controls. Data analyses were performed with CAT12/SPM12 applying general linear models for four morphometric measures: gyrification and cortical thickness (surface-based morphometry), and whole-brain grey matter/grey matter volume (voxel-based morphometry - VBM). Group effects were tested using age and gender as covariates (and total intracranial volume for VBM). Voxel-based morphometry analysis revealed a schizophrenia vs. control group effect on regional grey matter volume (p < 0.05, familywise error correction) in the right globus pallidus. There was no group effect on white matter volume when correcting for multiple comparisons neither on cortical thickness. Gyrification changes in clinical samples were found in the left supramarginal gyrus (BA40) - increased and reduced gyrification, respectively, in BPD and SCZ patients - and in the right inferior frontal gyrus (BA47), with a reduction in gyrification of the SCZ group when compared with controls. The joint analysis of different morphometric features, namely measures such as gyrification, provides a promising strategy for the elucidation of distinct phenotypes in psychiatric disorders. Different morphological change patterns, highlighting specific disease trajectories, could potentially generate neuroimaging-derived biomarkers, helping to discriminate schizophrenia from bipolar disorder in early phases, such as first-episode psychosis patients.

Hysteresis is a well-known phenomenon in physics that relates changes in a system with its prior history. It is also part of human visual experience (perceptual hysteresis), and two different neural mechanisms might explain it: persistence (a cause of positive hysteresis), which forces to keep a current percept for longer, and adaptation (a cause of negative hysteresis), which in turn favors the switch to a competing percept early on. In this study, we explore the neural correlates underlying these mechanisms and the hypothesis of their competitive balance, by combining behavioral assessment with fMRI. We used machine learning on the behavioral data to distinguish between positive and negative hysteresis, and discovered a neural correlate of persistence at a core region of the ventral attention network, the anterior insula. Our results add to the understanding of perceptual multistability and reveal a possible mechanistic explanation for the regulation of different forms of perceptual hysteresis.

Objective.To explore the viability of developing a computer-aided diagnostic system for Parkinsonian syndromes using dynamic [¹¹C]raclopride positron emission tomography (PET) and T1-weighted magnetic resonance imaging (MRI) data.Approach.The biological heterogeneity of Parkinsonian syndromes renders their statistical classification a challenge. The unique combination of structural and molecular imaging data allowed different classifier designs to be tested. Datasets from dynamic [¹¹C]raclopride PET and T1-weighted MRI scans were acquired from six groups of participants. There were healthy controls (CTRLn= 15), patients with Parkinson’s disease (PDn= 27), multiple system atrophy (MSAn= 8), corticobasal degeneration (CBDn= 6), and dementia with Lewy bodies (DLBn= 5). MSA, CBD, and DLB patients were classified into one category designated as atypical Parkinsonism (AP). The distribution volume ratio (DVR) kinetic parameters obtained from the PET data were used to quantify the reversible tracer binding to D2/D3 receptors in the subcortical regions of interest (ROI). The grey matter (GM) volumes obtained from the MRI data were used to quantify GM atrophy across cortical, subcortical, and cerebellar ROI.Results.The classifiers CTRL vs PD and CTRL vs AP achieved the highest balanced accuracy combining DVR and GM (DVR-GM) features (96.7%, 92.1%, respectively), followed by the classifiers designed with DVR features (93.3%, 88.8%, respectively), and GM features (69.6%, 86.1%, respectively). In contrast, the classifier PD vs AP showed the highest balanced accuracy (78.9%) using DVR features only. The integration of DVR-GM (77.9%) and GM features (72.7%) produced inferior performances. The classifier CTRL vs PD vs AP showed high weighted balanced accuracy when DVR (80.5%) or DVR-GM features (79.9%) were integrated. GM features revealed poorer performance (59.5%).Significance.This work was unique in its combination of structural and molecular imaging features in binary and triple category classifications. We were able to demonstrate improved binary classification of healthy/diseased status (concerning both PD and AP) and equate performance to DVR features in multiclass classifications.

The retina may serve as putative window into neuropathology of synaptic loss in Alzheimer’s disease (AD). Here, we investigated synapse-rich layers versus layers composed by nuclei/cell bodies in an early stage of AD. In addition, we examined the associations between retinal changes and molecular and structural markers of cortical damage. We recruited 20 AD patients and 17 healthy controls (HC). Combining optical coherence tomography (OCT), magnetic resonance (MR), and positron emission tomography (PET) imaging, we measured retinal and primary visual cortex (V1) thicknesses, along with V1 amyloid β (Aβ) retention ([11C]-PiB PET tracer) and neuroinflammation ([11C]-PK11195 PET tracer). We found that V1 showed increased amyloid-binding potential, in the absence of neuroinflammation. Although thickness changes were still absent, we identified a positive association between the synapse-rich inner plexiform layer (IPL) and V1 in AD. This retinocortical interplay might reflect changes in synaptic function resulting from Aβ deposition, contributing to early visual loss.

Background: It has been proposed that amyloid-β (Aβ) plays a causal role in Alzheimer’s disease (AD) by triggering a series of pathologic events—possibly including neuroinflammation—which culminate in progressive brain atrophy. However, the interplay between the two pathological molecular events and how both are associated with neurodegeneration is still unclear. Objective: We aimed to estimate the spatial inter-relationship between neurodegeneration, neuroinflammation and Aβ deposition in a cohort of 20 mild AD patients and 17 healthy controls (HC). Methods: We resorted to magnetic resonance imaging to measure cortical atrophy, using the radiotracer 11C-PK11195 PET to measure neuroinflammation levels and 11C-PiB PET to assess Aβ levels. Between-group comparisons were computed to explore AD-related changes in the three types of markers. To examine the effects of each one of the molecular pathologic mechanisms on neurodegeneration we computed: 1) ANCOVAs with the anatomic data, controlling for radiotracer uptake differences between groups and 2) voxel-based multiple regression analysis between-modalities. In addition, associations in anatomically defined regions of interests were also investigated. Results: We found significant differences between AD and controls in the levels of atrophy, neuroinflammation, and Aβ deposition. Associations between Aβ aggregation and brain atrophy were detected in AD in a widely distributed pattern, whereas associations between microglia activation and structural measures of neurodegeneration were restricted to few anatomically regions. Conclusion: In summary, Aβ deposition, as opposed to neuroinflammation, was more associated with cortical atrophy, suggesting a prominent role of Aβ in neurodegeneration at a mild stage of the AD.