Psychoradiology is an emerging field that applies medical imaging technologies to the analysis of mental health, neurophysiology and psychiatric conditions1. Psychoradiology is not currently validated for clinical practice (as of November 2016) and relies on imaging data analysis rather than visual inspection of images.

Imaging techniques of the brain and nervous system have improved in sophistication, sensitivity and definition over time. Previously in psychiatry only gross abnormalities could be detected with either the naked eye or computed tomography (eg. severe frontal lobe and traumatic brain injuries). Since CT of patients with schizophrenia identified bilateral ventricular enlargement in 1976, the volume of descriptions of structural abnormalities in mental illness has increased1. Modern imaging technologies such as magnetic resonance imaging (MRI) and derivatives (Functional MRI including resting state and task-based functional MRI, MR spectroscopy, perfusion mapping, the application of Diffusion-tensor imaging and tractography) have given rise to an increasing body of scientific literature that elucidates how various mental states, conditions and psychiatric diseases affect physical structures, their activity and neural circuits in the brain across time and treatment1.

Examples include (but are not limited to):

  • depression
    • MRI  data (Binary pattern classification, voxel-based morphometry) to predict response to ECT for acute major depressive disorder 2
      • Structural impairment in the subgenual cingulate cortex prior to therapy was positively correlated with response to ECT 2
      • Patients receiving ECT demonstrated an increase in hippocampal volume 2
  • bipolar I disorder
    • Whole-brain voxel-based analysis with diffusion tensor imaging showed white matter and structural abnormalities in the corpus callosum, tapetum, fornix and stria terminalis 3
  • borderline personality disorder
    • myriad and often contradictory neuroimaging findings 4 
    • decreased white matter integrity in the cingulum and fornix
    • association of anger with fractional anisotropy in the cingulum 5
    • association of affective instability and abandonment avoidance with fractional anisotropy in the fornix 5 
  • individual recognition and 'Fluid Intelligence' - "the capacity for on-the-spot reasoning to discern patterns and solve problems independently of acquired knowledge " 6,12
    • functional connectivity MRI used frontoparietal networks to accurately identify individuals from a pool of 126 subjects. 
    • individual connectivity profiles predicted fluid intelligence cognitive behaviour
  • schizophrenia
    • voxel-based morphometric changes, most consistently in the left superior temporal gyrus and left medial temporal lobe 7
    • Some studies have focused on premorbid, high-risk populations (eg Chang 2016) 8
  • some authors have queried the possibility of connectomes (cortical connectivity networks) guiding the monitoring of childhood development in the future 9

"...if our diagnostic categories have not been valid until now, then research of any type – epidemiological, etiological, pathogenetic, therapeutic, biological, psychological or social – if carried out with these diagnoses as inclusion criterion, is equally invalid." 10

                   - Univ. Prof. Heinz Katschnig, World Psychiatry Association, 2010

Imaging findings and post-mortem analyses have contributed to the understanding of biological pathophysiology and the underlying neural mechanisms of mental illnesses1. Some authors have recently suggested that imaging may change the diagnostic structure in psychiatry1.

Using a categorical approach to diagnosis of mental illness (such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or International Classification of Diseases), the presence of various symptoms and symptom constructs either at one point in time or over time are organised into categories, for example major depressive disorder, minor depressive disorder etc. This has been compared to taxonomy in botany; classifying a plant based on observation of the types and number of leaves (rather than any underlying factor) 10. In practice, there are spectra of patients that don’t necessarily fit neatly into these categories, with a given patient arguably suiting one DSM diagnosis over another, depending on the interpretation and weighting of individual symptoms and their time-course. 

A system of classifying and diagnosing psychopathology that reflects modern advances in neuroscience should assist in guiding research and treatment.  Some psychiatry academics already advocate moving from a categorical approach towards one that is more based in pathophysiological and objective findings 11. Such a system is proposed by the US National Institute of Mental Health for research purposes: the Research Domain Criteria (RDoC) 13

With a growing wave of objective digital imaging and tractographic data, a multimodal template for research in psychiatry (such as the RDoC), including objective anomalies of circuitry and cortical volume and activity will be increasingly significant and very likely will eventually guide ground level diagnosis and treatment of mental illness in the future. 

With the advent of ‘big data’ and the exponential increase in IT processing power and speed+, huge quantities of tractographic and cortical information are able to be collated and compared. This already occurs on a scale of tens of thousands of individual patients11, comparing controls with condition states, across age-groups, as well as analysing treatment effects (including pharmaceutical and non-pharmaceutical treatment). Theoretically, capacity for large-scale analyses will increase in the future with the burgeoning field of Quantum computing++.

Large-scale analysis of cerebral regional connectivity (ie the 'connectome') for the purpose of identifying regional abnormalities implicated in psychiatric disorders is complicated by the variability and unique 'fingerprint' of an individual 12. This characteristic connectivity and variation may be influenced by genes, perinatal events, life experiences and socioeconomic aspects 1. The Human Connectome Project (HCP) is a consortium of universities overseen by the USA National Institute of Health that strives to identify the neural pathway basis of human brain physiology.

HCP and other datasets and software for their analysis are open-source; released to the public for use by the scientific community.

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