Newcastle

Research Projects

The Australian Dementia Network (ADNeT)

ADNeT

The Australian Dementia Networks (ADNeT) is an NHMRC funded research program which is bringing together Australia’s leading researchers, clinicians and consumers to create a powerful network for dementia prevention, treatment and care. Michael Breakspear is heading the ADNeT technology arm partnering with CSIRO, providing a harmonised platform and protocols across state and territory jurisdictions for the acquisition, storage and sharing of data for dementia research – a first for Australia. Renate Thienel is coordination the Newcastle ADNeT Screening and Trials arm, which seeks to reduce the duration of clinical trials in order to fast-track the development of effective therapies to prevent or treat dementia. Léonie Borne is working as a post-doctoral fellow on ADNeT, using machine learning to developing algorithms to identify suitable participants for clinical trials.

PISA

Prospective Imaging Study of Ageing (PISA): Genes, Brain & Behaviour

The pathogenesis of Alzheimer's Disease (AD) begins several decades before the first cognitive symptoms appear. This preclinical phase of the disease is of tremendous interest since novel disease-modifying treatments may be possible during this period. However, as people at high future risk of AD are hard to identify, few studies have focused on it. The Prospective Imaging Study of Ageing (PISA) focus on healthy mid-life Australians at high genetic risk of developing AD to identify subtle cognitive and brain changes linked to the preclinical phase of AD.

The analyses made by Léonie Borne show:
  1. Evidence of a robust link between cognitive decline and widening of cortical folds associated with progression to dementia.
  2. Prediction of AD using algorithms trained on healthy mid-life and older adults.
  3. Evidence that memory tests alone are insufficient to study the preclinical stage of AD and that other cognitive tests, such as those related to executive functions, are required to best identify individuals at risk.

Functional and structural brain networks in persons at high risk of bipolar disorder.

This ongoing collaboration with researchers at the University of NSW and University of Cambridge is now in the follow-up stage broadening into a longitudinal study of brain changes in a high-risk cohort. With the objective now being to better understand the progression of bipolar disorder (BD), given unaffected first-degree relatives of patients with BD have an odds ratio of ~7–14 of developing BD.

Following on from previously published work on the initial brain imaging dataset (Perry et al. (2019), Frankland et al. (2018), Roberts et al. (2018), Jenagathan et al. (2018)) and other broader research, structural and functional dysconnection amongst key brain networks supporting cognitive and affective processes have been highlighted in both persons with BD diagnosis and high-risk relatives. Work currently under review aimed to compare longitudinal structural connectivity changes in individuals at high genetic risk for BD to those without a family history of mental illness. Network-based statistics revealed that on-top of shared maturation changes in structural connectivity over time, high risk participants showed a subset of regions with weakened connectivity as compared to age-matched controls. This work to provides insight into and may present a candidate for predicting conversion to BD.

bipolar
Psychosis

Control in an imprecise world: the cognitive control network, schizophrenia and active inference

Disruption and dysfunction of cognitive processes is a hallmark of schizophrenia, appearing in early stages of illness. The dysconnectivity theory suggests that the core pathology of cognitive deficits in schizophrenia is impairment in functional integration of neural systems.

This study utilises structural MR imaging to identify the inter-group differences between healthy controls and early psychosis patients, as well as covariates of task performance. Nikitas Koussis will study how individual differences lead to specific disease outcomes in early psychosis patients. Participants are scanned at baseline and 12 months to provide a late neurodevelopment perspective. Nikitas Koussis will examine associations of symptom severity and relapse to patterns of structural connectivity reconstructed from patients with early psychosis.

Identification of intrinsic brain network properties may lead to biomarkers for diagnostic outlook, leading to risk stratification and personalised therapy for this vulnerable population.

Predictive processing in the negative symptoms of psychosis

Is emotion really as simple as smiling when you’re happy, and frowning when you’re angry? The mind combines multimodal information, including seeing, hearing and feeling the outside world, the inner sensation of organs such as the heart and gut, and our previous experiences, to construct the moment-to-moment emotions that we feel.

Impairments in facial expressivity, motivation, and sociality are the most debilitating features of psychotic disorders, yet they are inadequately treated. In this study, Jayson Jenagathan is using facial emotion detection algorithms, functional brain imaging, and novel tasks including real-time facial feedback and heart rate feedback, to discover the changes in the brain’s emotional circuits that underlie these symptoms.

Public Research Articles Selection

Schultze-Kraft, Matthias, et al. “Exploiting the potential of three dimensional spatial wavelet analysis to explore nesting of temporal oscillations and spatial variance in simultaneous EEG-fMRI data.” Progress in biophysics and molecular biology 105.1-2 (2011): 67-79.

Alghowinem, Sharifa, et al. “Multimodal depression detection: fusion analysis of paralinguistic, head pose and eye gaze behaviors.” IEEE Transactions on Affective Computing 9.4 (2016): 478-490.

Frankland, Andrew, et al. “Comparing the phenomenology of depressive episodes in bipolar I and II disorder and major depressive disorder within bipolar disorder pedigrees.” The Journal of clinical psychiatry 76.1 (2015): 32-39.

Frankland, Andrew, et al. “Clinical predictors of conversion to bipolar disorder in a prospective longitudinal familial high-risk sample: focus on depressive features.” Psychological medicine 48.10 (2018): 1713-1721.

Gollo, Leonardo L., et al. “Fragility and volatility of structural hubs in the human connectome.” Nature neuroscience 21.8 (2018): 1107-1116.

Guo, C. C., et al. “Distinct neurobiological signatures of brain connectivity in depression subtypes during natural viewing of emotionally salient films.” Psychological medicine 46.7 (2016): 1535.

Iyer, Kartik K., et al. “Early detection of preterm intraventricular hemorrhage from clinical electroencephalography.” Critical care medicine 43.10 (2015): 2219-2227.

Valenzuela, Michael J., et al. “Posterior compensatory network in cognitively intact elders with hippocampal atrophy.” Hippocampus 25.5 (2015): 581-593.

Zalesky, Andrew, et al. “Connectome sensitivity or specificity: which is more important?.” Neuroimage 142 (2016): 407-420.