Milad Mashhady Ali Poury ¹, Ali Ameri ¹, Saeed Masoudnia ², Hosna Tavakoli^2,3, Faezeh Ghasemi^4,5, Reza Rostami⁶, and Mohammad-Reza Nazem-Zadeh^2,7
1Department of Biomedical Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Islamic Republic of), ²Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ³cognitive neuroscience, Institute for Cognitive Science Studies, Tehran, Iran (Islamic Republic of), ⁴Medical Physics and Engineering, Shahid Beheshti University of Medical Sciences, Tehran, Iran (Islamic Republic of), ⁵Research Center for Biomedical Technologies and Robotics, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of), ⁶Department of Psychology, University of Tehran, Tehran, Iran (Islamic Republic of), ⁷Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences, Tehran, Iran (Islamic Republic of)

We developed a predicting algorithm based on brain connectivity to quantify the altered brain regions in schizophrenia, bipolar, and attention deficit hyperactivity disorders, to help diagnose them using neuroimaging biomarkers. Functional connectivity was utilized to construct brain graphs, on which the node2vec framework was applied to produce the node embeddings. The concatenation of embeddings was used to derive the region feature vectors to feed support vector machine (SVM) classifiers. Also, we build a model to assist the diagnosis of disorders using a weighted voting ensemble. The achieved accuracy proved to outperform to the state-of-the-art models.

Eirini Messaritaki¹, Sonya Foley¹, Kali Barawi², and Derek K Jones^1
1Psychology, Cardiff University, Cardiff, United Kingdom, ²Medicine, Cardiff University, Cardiff, United Kingdom

This work explores the link between brain structural connectivity and schizotypy in healthy participants. We found evidence of higher structural connectivity in the sensorimotor network of high-schizotypy participants compared to those with low schizotypy. Weaker evidence was also found for alterations in the auditory, default-mode and visual networks. In contrast, no significant differences were found in the structural connectivity of the whole-brain or limbic networks. The higher connectivity possibly shields these participants from schizophrenia. Our results agree with the results of works proposing that deficiencies in the sensorimotor brain network should be investigated as possible causes for schizophrenia.

Anouk Schrantee¹ and Ottavia Dispasquale^2
1Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, Netherlands, ²Department of Neuroimaging, Institute of Psychiatry, King's College London, London, United Kingdom

While citalopram pharmacology has been extensively investigated and defined, the molecular mechanisms underlying brain functional response to this antidepressant are less well characterised. Therefore, we explored the functional response of the healthy brain to an acute citalopram challenge by enriching resting-state fMRI with molecular templates of serotonin neurotransmitters. We found that a high oral dose of citalopram induces significant alterations in 5-HT1A-enriched functional connectivity, and that serotonin transporter occupancy is linked with the interindividual functional response to citalopram in the SERT-related network. These findings set a new basis for a mechanistic understanding of the functional effects of citalopram in depression.

Filomena Grazia Alvino¹, Silvia Gini¹, Alberto Galbusera¹, David Sastre¹, Federico Rocchi¹, Marco Pagani¹, Caterina Montani¹, Francesco Papaleo², Massimo Pasqualetti^1,3, Carrie Bearden⁴, and Alessandro Gozzi^1
1Italian Institute of Technology, Rovereto (TN), Italy, ²Italian Institute of Technology, Genova, Italy, ³Department of Biology, Unit of Cell and Developmental Biology, University of Pisa, Pisa, Italy, ⁴Department of Psychiatry and Biobehavioral Sciences and psychology Semel Institute for Neuroscience and Human Behavior, UCLA, Los Angeles, CA, United States

22q11.2 Deletion Syndrome (22q11DS), a major risk factor schizophrenia and autism, is often associated with disrupted brain connectivity. However, the developmental and neural underpinnings of brain connectopathy in 22q11DS remain unclear. Using mouse fMRI, we found that 22q11DS-related dysconnectivity exhibits a stereotypical developmental trajectory, with widespread functional hyperconnectivity in pre-pubertal mice reverting to focal fronto-hippocampal hypoconnectivity in adulthood. Notably, juvenile hyperconnectivity, but not adult hypoconnectivity, was completely rescued by a GSK3β antagonist. Finally, guided by our mouse model data, we identified a similar developmental trajectory in 22q11DS patients, hence corroborating the translational validity of these findings.