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From number crunching to brains: my experiences of interdisciplinary research

by Michelle Taylor @chelle_bluebird

From TARG to neuroscience

The final six months of a PhD can be a stressful time. Not only are you trying to write up three years of research, wondering whether you have done enough work, but you also need to consider what to do next. I decided to try my hand at something different…

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My PhD was in the area of epidemiology, where I was using large datasets (such as the Avon Longitudinal Study of Parents and Children, based here at the University of Bristol) to determine causes and consequences of using various drugs of abuse. My time was mainly spent designing and conducting statistical analyses on data that had already been collected and were available for secondary analysis. I completed this work in TARG and the School of Social and Community Medicine and was lucky enough to be funded by the Wellcome Trust on a PhD programme in molecular, genetic and lifecourse epidemiology. The Wellcome Trust also fund two other PhD programmes at the University of Bristol, one in ‘Neural Dynamics’ and another in ‘Dynamic Cell Biology’. Towards the end of my PhD an opportunity arose – the Elizabeth Blackwell Institute were offering three researchers fellowships to conduct nine months of research with one of the other Wellcome Trust programmes. This would involve changing research area and learning something completely new – and I decided to go for it. I applied to move to the Neural Dynamics programme. As my past research had focused on addiction and mental health, gaining knowledge of the field of neuroscience seemed fitting.

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After identifying a potential new supervisor and quickly putting together and submitting an application I was told that I had been successful. I was go
ing to become a neuroscientist for the next nine months. The day after handing in my PhD I headed off to the lab of Matt Jones, a neuroscientist whose research interests include sleep, memory and brain circuitry. I was going to be working on a study that aimed to find out more about how genes influence overnight brain activity and memory in humans. I’ve written a little more about this study at the end of this blog post, just in case you’re interested!

My new lab group were very friendly and welcoming, although at times it seemed like they were talking in a different language. I would attend seminars in my new department and be completely confused within minutes. While I did have some knowledge of neuroscience from reading literature, my knowledge was severely lacking compared to that of my new colleagues. Mind you, I could always get my own back by blinding them with statistics!

 

The study involved getting participants to stay in our sleep clinic overnight and measuring their brain activity while they slept. I had to learn new methods of data collection, which involved measuring a person’s head to find specific points and gluing on electrodes to measure their brain activity (known as PSG, or polysomnography) [1]. Once these data were collected, the night’s recording needed to be scored into various stages of sleep. We can determine this from the length, height and frequency of the waves on the sleep recording. There are two main stages of sleep: REM (which stands for rapid eye movement) and non-REM. Non-REM can be broken down further into stages 1, 2 and 3 [2,3]. Stage 3 is the deepest stage of sleep, while stage 2 contains oscillations called spindles and K-complexes which are thought to play a role in memory consolidation while we sleep [3]. Learning to score a night’s sleep was something very new to me. I was used to having my data in the form of numbers in a spreadsheet not as wavy lines dominating the computer screen!

brain_activity

At the end of the nine months, I found myself understanding the talks that I went to – I even started to sound like a neuroscientist myself at times. Many things which originally seemed overwhelming (such as collecting PSG data) now feel like second nature, and the wavy lines on a computer screen are now meaningful. While at first the experience seemed daunting, it has no doubt opened my mind and expanded my knowledge. The ability to conduct interdisciplinary research is a well-regarded asset, but this experience has not only enhanced my CV. It has increased my confidence when talking to other researchers, as I have realised that we can all learn something from one another. Most importantly I have learned to look at research from a broader perspective – what does my research mean for other fields? How can it inform other research that is different from my own? It is, of course, combining the answers to all of these questions that will enhance science and in turn have more impact on the wider world.

My neuroscience experience has come to an end and for now, it is back to epidemiology. But I will definitely look back on my time in neuroscience fondly, and, who knows, I might even get the chance to integrate epidemiological research with neuroscience in the future…

 

A little more about the study

Different parts of our brains communicate with one another as we learn new information during the day. Overnight brain activity then helps us to file memories for long-term storage. Evidence suggests that this process varies naturally in everyone. To help us understand what causes this variation, we are interested in finding out more about how genes influence overnight brain activity in healthy individuals. Studying our genes by specifically testing those who carry particular (naturally occurring) form of them can help us understand their role in shaping the natural variation we see in brain activity. Importantly, understanding this in healthy people can then go on to help us develop new targets for treatments to help the sick. We therefore carried out a study to look at how naturally occurring variation at a particular gene variant affects memory consolidation during sleep.

The gene variant was chosen based on previous studies that have shown that it affects both brain activity and sleep. To do this, we invited back participants from the Avon Longitudinal Study of Parents and Children who had provided us with a DNA sample. Information about their genes had been processed and based on this information they were identified as being carriers or non-carriers of the gene we were interested in. This is a study design known as ‘recall-by-genotype’. We then asked these people to spend two nights in a sleep laboratory, perform some memory based tasks and complete some questionnaires so that we can measure how genetic differences relate to memory and brain activity during sleep.

motionwatch_wrist_smlWhilst participants were in the sleep facility we attached a number of sensors to their head in order to record their brain waves, eye movements and muscle activity. We also used sensors on the chest to measure heart rate and take video and audio recordings to confirm whether or not participants become unsettled during the night. Participants were asked to complete some questionnaires about their sleep behaviour and to carry out a memory task before and after sleep.

For the two weeks in between visits to the sleep laboratory, we asked participants to wear an ‘actiwatch’. An actiwatch looks like a normal watch and records movement, telling us when the participant usually goes to sleep and wakes up. We asked participants to wear the actiwatch on their wrist at all times and asked them to fill in a sleep diary for the two weeks.

What do we hope to find?

actigraphyWe hope to find that individuals who carry our genetic variant of interest differ from those who do not carry the variant on a range of sleep characteristics including the non-REM stage 2 spindles and slow wave oscillations found on stage 3 of non-REM sleep. We also expect to find difference between genotype groups on ability to complete the memory task, and the speed at which they complete the memory task. Finally, we expect to observe a correlation between the stage 2 sleep spindles and the results of the memory task. If we observe these results in our data, then this will suggest that this genotype can influence brain activity during sleep which then in turn can effect a person’s memory, as this memory is not being consolidated as well over night.

 

Where can I find out more?

A protocol for this study has already been published [4].
Once completed, this study will be published open access within a scientific journal.

References:

[1] Wikipedia – polysomnography

[2] American association of sleep

[3] Wikipedia – sleep (including information on stages, spindles, K-complexes and slow waves)

[4] Hellmich C, Durant C, Jones MW, Timpson NJ, Bartsch U, Corbin LJ (2015) Genetics, sleep and memory: a recall-by-genotype study of ZNF804A variants and sleep neurophysiology. BMC Med Genet 16:96

Psychiatric disorders: what’s the significance of non-random mating?

7960674098_2070f1fe64_bHardly a week passes without the publication of a study reporting the identification of genetic variants associated with an increasing number of behavioural and psychiatric outcomes. This partly driven by the growth in large international consortia of studies, as well as the release of data from very large studies such as UK Biobank. These consortia and large individual studies are now achieving the necessary sample sizes to detect the very small effects associated with common genetic variants,.

We’ve known for some time that psychiatric disorders are under a degree of genetic influence, but one puzzle is why estimates of the heritability of these disorders (i.e., the proportion of variability in risk of a disorder that is due to genetic variation) differs across disorders. Another intriguing question is why there appears to be a high degree of genetic comorbidity across different disorders; that is, common genetic influences that relate to more than one disorder. One possible answer to both questions may lie in the degree of non-random mating by disorder.

Non-random mating refers to the tendency for partners to be more similar than we would expect by chance on any given trait of interest. This is straightforward to see for traits such as height and weight, but less obvious for traits such as personality. A recent study by Nordsletten and colleagues investigated the degree of non-random mating for psychiatric disorders, as well as a selection of non-psychiatric disorders for comparison purposes.

 

Methods

The researchers used data from three Swedish national registers, using unique personal identification numbers assigned at birth. The data were linked to the Swedish National Patient Register (NPR), which includes diagnostic information on all individuals admitted to a Swedish hospital and, since 2001, on outpatient consultations. Individuals with multiple diagnoses could appear as a “case” in each separate analysis of these different diagnoses.

Cases of schizophrenia, bipolar disorder, autism spectrum disorder, anorexia nervosa, substance abuse, attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), major depressive disorder, social phobia, agoraphobia, and generalised anxiety disorder were identified using standard protocols. For comparison purposes, cases of Crohn’s disease, type 1 and type 2 diabetes, multiple sclerosis and rheumatoid arthritis were also identified.

For each case (i.e., individuals with a diagnosis), five population controls were identified, matched on age, sex and area of residence. Mating relationships were identified through records of individual marriages, and through records of individuals being the biological parent of a child. The use of birth of a child was intended to capture couples who remained unmarried. For each member of a mated case pair a comparison sample was again generated, with the constraint that these controls not have the diagnosis of interest.

First, the proportion of mated pairs in the full case and control samples was summarised. Correlations were calculated to evaluate the relationship between the diagnostic status of each individual in a couple, first within and then across disorders. Logistic regression was used to estimate the odds of any diagnoses in mates of cases relative to mates of controls. Finally, the odds of any diagnosis in mates was estimated, and the relationship between the number of different disorders in a case and the presence of any psychiatric diagnoses in their mate explored.

Non-random mating is not a lack of promiscuity, it's the tendency for partners to be more similar than we would expect by chance on any given trait of interest.

Non-random mating is not a lack of promiscuity! It’s the tendency for partners to be more similar than we would expect by chance on any given trait of interest.

Results

Cases showed reduced odds of mating relative to controls, and this differed by diagnosis, with the greatest attenuation among individuals with schizophrenia. In the case of some diagnoses (e.g., ADHD) this low rate of mating may simply reflect, at least in part, the young age of these populations.

Within each diagnostic category, there was evidence of a correlation in diagnostic status for mates of both sexes (ranging from 0.11 to 0.48), and there was also evidence of cross-disorder correlations, although these were typically smaller than within-disorder correlations (ranging from 0.01 to 0.42).

In general, if an individual had a diagnosis this was typically associated with a 2- to 3-fold increase in the odds of his or her mate having the same or a different disorder. This was particularly pronounced for certain conditions, such as ADHD, autism spectrum disorder and schizophrenia.

In contract to psychiatric samples, mating rates were consistently high among both men and women with non-psychiatric diagnoses, and correlations both across and within the conditions was rare (ranging from -0.03 to 0.17), with the presence of a non-psychiatric diagnosis associated with little increase in his or her spouse’s risk.

This general population study found an amazing amount of assortative (non-random) mating within psychiatric disorders.

This general population study found an amazing amount of assortative mating within psychiatric disorders.

Conclusions

These results indicate a striking degree of non-random mating for psychiatric disorders, compared with minimal levels for non-psychiatric disorders.

Correlations between partners were:

  • Greater than 0.40 for ADHD, autism spectrum disorder and schizophrenia,
  • Followed by substance abuse (range 0.36 to 0.39),
  • And detectable but more modest for other disorders, such as affective disorders (range 0.14 to 0.19).

The authors conclude the following:

  • Non-random mating is common in people with a psychiatric diagnosis.
  • Non-random mating occurs both within and across psychiatric diagnoses.
  • There is substantial variation in patterns of non-random mating across diagnoses.
  • Non-random mating is not present to the same degree for non-psychiatric diagnoses.

Implications

So, what are the implications of these findings?

First, non-random mating could account for the relatively high heritability of psychiatric disorders, and also explain why some psychiatric disorders are more heritable then others (if the degree of assortment varies by disorder).

This is because non-random mating will serve to increase additive genetic variation across generations until equilibrium is reached, leading to increased (narrow sense) heritability for any trait on which it is acting.

Second, non-random mating across psychiatric disorders (reflected, for example in a correlation of 0.31 between schizophrenia and autism spectrum disorder) could help to explain in part the observed genetic comorbidity across these disorders.

Non-random mating could explain why some psychiatric disorders are more heritable then others.

Non-random mating could explain why some psychiatric disorders are more heritable than others.

Strengths and limitations

This is an extremely well-conducted, authoritative study using a very large and representative data set. The use of a comparison group of non-psychiatric diagnoses is also an important strength, which gives us insight into just how strong non-random mating with respect to psychiatric diagnoses is.

The major limitations include:

  • Not being able to capture other pairings (e.g., unmarried childless couples)
  • A reliance on register diagnoses, which largely excludes outpatients etc
  • A lack of insight into possible mechanisms

This last point is interesting; non-random mating such as that observed in this study could arise because couples become more similar over time after they have become a couple (e.g., due to their interactions with each other) or may be more similar from the outset (e.g., because similar individuals are more likely to form couples in the first place, known as assortative mating).

The authors conclude that the non-random mating they observed may be due toassortative mating for two reasons. First, shared environment (which would capture effects of partner interactions) appears to play very little role in many psychiatric conditions. Second, neurodevelopmental conditions are present over the lifespan (i.e., before couples typically meet), which would suggest an assortative mating explanation for the observed similarity for at least these conditions.

Some disorders (e.g., schizophrenia) are associated with reduced reproductive success, and therefore should be under strong negative selection in the general population. However, these results suggest they may be positively selected for within certain psychiatric populations. In other words, these mating patterns could, in part, compensate for the reduced reproductive success associated with certain diagnoses, and explain why they persist across generations.

Implications for future research

Non-random mating also has implications for research, and in particular the use of genetic models. These models typically assume that mating takes place at random, but the presence of non-random mating (as indicated by this study) suggests that this should be taken into account in these models. This could be done by allowing for a correlation between partners, and neglecting this correlation may lead to an underestimate of heritability.

Summary

This study suggests that non-random mating is widespread for psychiatric conditions, which may help to provide insights into why these conditions are transmitted across generations, and why there is such a strong degree of comorbidity across psychiatric diagnoses. The results also challenge a fundamental assumption of many genetic approaches.

Assortative mating means that the person closest to an individual with a psychiatric disorder is also likely to have psychiatric problems.

Assortative mating means that, in general population terms, people in romantic relationships with those who have psychiatric disorders are also likely to have psychiatric problems themselves.

Links

Primary paper

Nordsletten AE, Larsson H, Crowley JJ, Almqvist C, Lichtenstein P, Mataix-Cols D. (2016) Patterns of nonrandom mating within and across 11 major psychiatric disorders. JAMA Psychiatry 2016. doi: 10.1001/jamapsychiatry.2015.3192

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Treatment for depression in traumatic brain injury: Cochrane find no evidence for non-pharmacological interventions

by Eleanor Kennedy @Nelllor_

This blog originally appeared on the Mental Elf site on 31st May 2016.

Traumatic Brain Injury has been associated with increased occurrence of depression (Gertler et al, 2015). Traumatic Brain Injury results from damage to the brain by external forces, such as direct impact or rapid acceleration; consequences of a traumatic brain injury may be temporary or permanent and can lead to problems with cognition, emotion and behaviour (Maas, Stocchetti, & Bullock, 2008).

The main feature of depression is either a depressed mood or loss of interest and pleasure in usual activities, or both, consistently for a two week period. Depression can present as a major risk factor for suicide after Traumatic Brain Injury.

A recent Cochrane systematic review aimed to measure “the effectiveness of non-pharmacological interventions for depression in adults and children with Traumatic Brain Injury at reducing the diagnosis and severity of symptoms of depression.”

People who experience traumatic brain injury are at an increased risk of depression.

People who experience traumatic brain injury are at an increased risk of depression.

Methods

The Cochrane Injuries Group searched eight electronic databases for randomised controlled trials (RCTs) of non-pharmacological interventions for depression in adults and children who had a Traumatic Brain Injury. For inclusion in the review, study participants had to fulfil the following criteria:

  • A history of Traumatic Brain Injury due to external forces; samples that included participants with non-traumatically acquired brain injury, such as stroke, were also included if the data allowed for separate analysis of those with Traumatic Brain Injury
  • Fulfilment of diagnostic criteria for an applicable mood disorder, such as major depressive disorder or adjustment disorder with depressive mood, based on DSM or ICD criteria
  • Presenting with clinically significant depressive symptoms based on standardised measures

The primary outcome was “the presence or remission of depressive disorders, as determined by the use of accepted diagnostic criteria (e.g. DSM-IV or ICD-10), by the use of a standardised structured interview based on such criteria (e.g. Structured Clinical Interview for the DSM Disorders), or the results of validated self- or observer-rated questionnaires of depressive symptoms.”

The secondary outcomes were:

  • Neuropsychological functioning, psychosocial adjustment, everyday functioning, quality of life, and participation
  • Medication and healthcare service usage
  • Treatment compliance, based on the proportion of withdrawals from intervention
  • The occurrence of suicide or self-harm
  • Any adverse effects of the intervention.

Results

Six studies were included in the review. Three of the studies were carried out in the USA (Ashman, Cantor, Tsaousides, Spielman, & Gordon, 2014; Ashman & Tsaousides, 2012; Fann et al., 2015; Hoffman et al., 2010), one in China (He, Yu, Yang, & Yang, 2004), one in Canada (Bedard et al., 2014) and one in Australia (Simpson, Tate, Whiting, & Cotter, 2011). Participants in all studies were over 18 years of age.

Summary of interventions in included studies

Study NParticipants Intervention Duration of Treatment Outcome measure
Ashman 2014 (Ashman et al., 2014; Ashman & Tsaousides, 2012) 77(43 completed) Cognitive Behaviour Therapy (CBT) or Supportive Psychotherapy (SPT) 16 sessions over 3 months Structured Clinical Interview for DSM-IVBeck Depression Inventory – second edition (BDI-II)
Bedard 2013 (Bedard et al., 2014) 105(76 completed) Mindfulness-based cognitive therapy (MBCT) modified to suit those with TBI 10 weekly session plus recommended daily meditation BDI-II
Fann 2015 (Fann et al., 2015) 100(86 with follow up data) CBT in person or by telephone 8 to 12 weekly sessions Hamilton Depression Rating Scale (HAMD-17)
He 2004 (He et al., 2004) 64(63 completed) Repetitive transcranial magnetic stimulation (rTMS) 4 treatment sessions each lasting 5 days, with an interval of 2 days between sessions HAMD
Hoffman 2010 (Hoffman et al., 2010) 80(76 completed) Supervised exercise training 10 weekly sessions, plus a home program BDI
Simpson 2011 (Simpson et al., 2011) 17(16 completed) Group-based CBT 10 weekly sessions Hospital Anxiety and Depression Scale (HADS)

Primary outcomes

The review reported on four comparative analyses:

  1. CBT, or a variant of CBT, vs waiting list; included a meta-analysis of three studies (Bedard, Fann, Simpson). There was no indication of a difference in depression symptoms attributable to the intervention (standardised mean difference (SMD) -0.14, 95% CI -0.47 to 0.19; Z = 0.83, p = .41).
  2. CBT to SPT; based on one study (Ashman), the difference in depression remission was not statistically supported (RR 0.76; 95% CI 0.58 to 1.00; Z = 1.96; P = 0.05) nor was the difference between groups in depression symptoms (SMD -0.09; 95% CI -0.65 to 0.48; Z = 0.30; P = 0.77).
  3. rTMS plus tricyclic antidepressants (TCA) to TCA; based on one study (He). There was a reduction in depression symptoms seen in the rTMS plus TCA group, (0.84; 95% CI -1.36 to -0.32; Z = 3.19; P = 0.001), however the difference was not considered to be clinically relevant. This was the only study to report adverse effects as two participants reported transient tinnitus with spontaneous remission.
  4. Supervised exercise and exercise as usual; based on a single study (Hoffman). There was no difference in depression symptoms between groups following the intervention (SMD -0.43; 95% CI -0.88 to 0.03; Z = 1.84; P = 0.07).

Secondary outcomes

Secondary outcomes were reported for each individual study. There was no difference in treatment compliance between intervention and comparison group in each study. One study (He et al., 2004) reported adverse effects as two participants reported transient tinnitus with spontaneous remission.

Most other secondary outcomes showed no difference between intervention and treatment groups.

There is insufficient evidence to recommend any particular non-pharmacological treatment for depression in traumatic brain injury.

There is insufficient evidence to recommend any particular non-pharmacological treatment for depression in traumatic brain injury.

Strengths and limitations

Some studies were not included because of the narrow focus of the review. The primary outcome of these studies was quality of life or psychological well-being and as such did not require included participants to have a diagnosis of depression or a particular cut-off score on a depression scale. While these may have been of interest, this is not necessarily a limitation as it allowed the authors to concentrate on a clinically relevant treatment effect for depression.

The authors found the quality of evidence to be low or very low in all comparisons, mainly due to the lack of blinding participants and personnel to the treatment. This lack of blinding could have affected the self-report depression symptom scales in particular. The authors suggested some suitable placebo treatments such as sham rTMS to imitate real TMS or a social contact intervention to compare to CBT.

Conclusion

The paucity of studies included makes it difficult to draw any firm conclusions. There was no strong evidence to support any of the interventions explored here. All of the studies are very recent which suggests there may be an increase in this kind of research.

The authors point to some implications for future research in this area, such as the careful consideration of what will be meaningful to the individual participants and the question of the suitability of RCT design for CBT interventions.

The review calls for future RCTs that compare active interventions with controls that replicate the effect of the attention given to participants during an active treatment.

The review calls for future RCTs that compare active interventions with controls that replicate the effect of the attention given to participants during an active treatment.

Links

Primary paper

Gertler P, Tate RL, Cameron ID. (2015) Non-pharmacological interventions for depression in adults and children with traumatic brain injury. Cochrane Database of Systematic Reviews 2015, Issue 12. Art. No.: CD009871. DOI: 10.1002/14651858.CD009871.pub2.

Other references

Ashman, T., Cantor, J. B., Tsaousides, T., Spielman, L., & Gordon, W. (2014). Comparison of cognitive behavioral therapy and supportive psychotherapy for the treatment of depression following traumatic brain injury: A randomized controlled trial. Journal of Head Trauma Rehabilitation, 29(6), 467–478. [PubMed abstract]

Ashman, T., & Tsaousides, T. (2012). Cognitive behavioral therapy for depression following traumatic brain injury: FINDINGS of a randomized controlled trial. Brain Impairment. Cambridge University Press.

Bedard, M., Felteau, M., Marshall, S., Cullen, N., Gibbons, C., Dubois, S., … Moustgaard, A. (2014). Mindfulness-based cognitive therapy reduces symptoms of depression in people with a traumatic brain injury: results from a randomized controlled trial. J Head Trauma Rehabil, 29(4), E13–22. [PubMed abstract]

Fann, J. R., Bombardier, C. H., Vannoy, S., Dyer, J., Ludman, E., Dikmen, S., … Temkin, N. (2015). Telephone and in-person cognitive behavioral therapy for major depression after traumatic brain injury: a randomized controlled trial. Journal of Neurotrauma, 32(1), 45–57. [PubMed abstract]

He, C. S., Yu, Q., Yang, D. J., & Yang, M. (2004). Interventional effects of low-frequency repetitive transcranial magnetic stimulation on patients with depression after traumatic brain injury. Chinese Journal of Clinical Rehabilitation, 8, 6044–6045.

Hoffman, J. M., Bell, K. R., Powell, J. M., Behr, J., Dunn, E. C., Dikmen, S., & Bombardier, C. H. (2010). A randomized controlled trial of exercise to improve mood after traumatic brain injury. Physical Medicine and Rehabilitation, 2(10), 911–919. [PubMed abstract]

Maas, A. I. R., Stocchetti, N., & Bullock, R. (2008). Moderate and severe traumatic brain injury in adults. Lancet Neurology, 7 (August), 728 – 741. [PubMed abstract]

Simpson, G. K., Tate, R. L., Whiting, D. L., & Cotter, R. E. (2011). Suicide prevention after traumatic brain injury: a randomized controlled trial of a program for the psychological treatment of hopelessness. The Journal of Head Trauma Rehabilitation, 26(4), 290–300. [PubMed abstract]

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– See more at: http://www.nationalelfservice.net/mental-health/depression/treatment-for-depression-in-traumatic-brain-injury-cochrane-find-no-evidence-for-non-pharmacological-interventions/#sthash.oqwXaf7W.dpuf