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Chairman Guy Goodwin introduced not only the speakers but also the audience to Pigeonhole live – the audience being enthusiastically encouraged to make use of their smart phones throughout this interactive meeting both to ask and answer questions via this medium. Traditional question cards were provided for the technologically-challenged!
Cognition needs to be treated differently from the other symptoms associated with MDD, Professor Raymond Lam, University of British Columbia, Canada, told the audience. Cognitive dysfunction in patients with MDD often persists into remission, and it is recognised that cognitive impairment drives functional impairment and, in particular, poor work functioning. This in turn means that patients who return to work are often working sub-optimally and this has associated costs – both financial costs for society but also costs in terms of the patient’s wellbeing. Work is important to patients, not just because of the money they earn – but as a source of accomplishment, intellectual stimulation, regular activity and social interaction.
As cognitive deficits affect a patient’s ability to functionally recover, new treatment options are needed to target cognitive dysfunction and better improve functional outcomes in patients with depression.
Which test is best in the assessment of cognitive dysfunction in MDD? This was the question posed by Dr John Harrison, VU University Medical Center, Amsterdam, The Netherlands and Metis Cognition Ltd.
An ideal test should be reliable, sensitive, valid, suitable for use in the long term, available in parallel forms and suitable for cross-cultural use. Dr Harrison cautioned the audience to do three things: choose the test to answer the question you want answered, remember that exploration informs confirmation and, most importantly, never be a slave to dogma. As he explained, tests to assess cognitive functioning in Alzheimer’s disease are insufficient and prone to a number of problems, yet they failed for 20 years because researchers were slaves to dogma!
In an assessment of the FOCUS study, cognition was successfully evaluated using the Digit Symbol Substitution Test (DSST) – suggesting that this would be a good candidate test for the assessment of cognitive dysfunction in MDD.
Interestingly, a Pigeonhole survey conducted during the meeting suggested that 70% of attendees already investigated cognitive deficits in their patients with MDD on a regular basis. However, 30% of attendees did not assess cognition regularly in their patients with MDD. Maybe those that don’t currently assess cognition in depression will find the THINC® Cognition Tool of interest.
Despite the increasing recognition of the importance of the assessment of cognition in MDD, as Professor Roger McIntyre, University of Toronto, Canada reported, no accepted and validated screening tool for the objective and subjective assessment of cognitive dysfunction in MDD suitable for use in daily clinical practice is currently available. Indeed, as he stated, what is needed is a tool to measure the extent of a deficit, not just aid in its identification.
This is the underlying rationale for the development of the THINC® Cognition Tool - a tool specifically developed to detect and measure cognitive dysfunction in MDD. The THINC® Cognition Tool incorporates several brief, easy to administer objective tests including the DSST, Choice Reaction Time (CRT), the Trail Making Test B (TMT-B), the One-Back Test (1BT) and the Pathfinder test as well as a subjective, patient reported assessment PDQ test. During the symposium a video demonstrating the objective tests was screened – showing the tests to be attractively designed and appealing to perform.
Currently, the THINC® Cognition Tool is being validated for the screening of cognitive dysfunction in adults with depression at the University of Toronto, Canada. It (along with many other useful materials concerned with cognitive deficiency in MDD) will be available to download from the THINC® website soon (THINCcognition.com). The tool will be free of charge and should be also be available in local languages.
‘Cognitive dysfunction in depression: are we THINC®ing about it enough?’ was the title of a well-attended satellite symposium sponsored by Lundbeck which took place on Sunday afternoon.
Artificial intelligence is a term that conjures up images of robots and machines capable of intelligent thought. In the field of psychiatry, artificial intelligence principles and practices are being used to take neuroimaging data and develop biomarkers that could support a clinical diagnosis and quantify and describe that diagnosis.
Dr Mitsuo Kawato of the ATR Computational Neuroscience Laboratories in Kyoto, Japan, has been involved in research that for the first time defines the biological dimension – an imaging dimension – of a psychiatric diagnosis. Research published by Dr Kawato and colleagues in Nature Communications in 2016 ranked among the top 1% of viewed scientific papers published in same period. Dr Kawato believes this is because the paper dealt with an artificial intelligence application that could define autism spectrum disorder (ASD) using neuroimaging-based classifiers – or biomarkers. The research team are using the application to define and create neuroimaging biomarkers for major depressive disorder (MDD), schizophrenia, obsessive compulsive disorder (OCD) and chronic pain syndromes.
Taking data from 200 patient samples, and looking at 10,000 neuroimaged connections and 140 brain lesions, Dr Kawato said it had been possible, using sophisticated artificial intelligence algorithms, to select the 16 functional connections that are specific for and discriminate ASD from normal (typically developed) brains. He explained that this type of computational neuroscience allows the description of one scale – one dimension – that plots the Gaussian distribution for typically developed individuals and another that plots the distribution for, in this case, ASD. This biomarker tool therefore also allows for a quantitative assessment – or score for the diagnosis.
What is more, Dr Kawato explained that these neuroimaging biomarkers being developed not only define and describe a specific diagnosis, but highlight distinctions between diagnoses and similarities and closeness of certain diagnoses. For example he said that the ASD neuroimaging biomarker could not distinguish MDD patients from their controls, but showed some ability to discriminate between patients with schizophrenia and controls. According to Dr Kawato, this closeness between ASD and schizophrenia is in keeping with historical views that these conditions shared some commonality and with genetic studies indicating common loci for schizophrenia and ASD.
Dr Kawato believes that neuroimaging biomarkers will be a valuable support to clinical diagnosis and will become a reality of practice and diagnosis in the near future. He told Progress in Mind: “In other disciplines of medicine like cardiovascular medicine and oncology for example, it is common to examine biomarkers – be they blood biomarkers or imaging scans (fMRI and PET). But in psychiatry, we haven’t had that kind of quantitative measurement to support the clinical diagnosis.”
“We had two objectives. The first was to provide objective scales to support clinical diagnosis with neuroimaginging based biomarkers.” Dr Kawato then said that if these biomarkers prove to be really reliable, it may be possible to use these tools to explore and describe the neurocircuits and brain regions with correlates for predicting certain diagnosis.
Dr Kawato described how the neuroimaging biomarker looks at resting state fMRI, with a 5-10 minute scan providing the data to allow a quantitative diagnosis. He said that in the future it might be possible, not just to diagnose one condition, but to use all the available biomarker scales to see if, in the case of ASD for example, a person has a condition more predominantly located between ASD and schizophrenia – or located closer to the normal healthy position.
These developments in neuroimaging biomarker research might also have applications in the management of psychiatric disorders. Dr Kawato said that real-time feedback based on imaging biomarkers might have therapeutic applications in some diagnoses. He explained: “We can define “ASDness” and then in realtime we can feed this back to our patient as a score. It’s something a bit like cognitive behavioural therapy or psychotherapy, although a little bit more high tech.” Dr Kawato said that pilot studies in ASD, MDD and chronic pain conditions, have been looking at this real-time feedback. According to Dr Kawato, outcomes may depend on the learning capability of a given patient and some conditions may be more amenable to reinforcement conditioning than others.
For the future, Dr Kawato said that neuroimaging biomarkers may soon become a clinical reality and he hoped that machine learning algorithms based on biomarkers might also find a place as another modality in the management of psychiatric disorders.
Computational neuropsychiatry is a new discipline, exciting huge interest for its potential in diagnosis and possibly even management.