Monthly Archives: March 2010

Review: Attitudes to Mental Illness 2010

The document reviewed here is a Department of Health document  ‘Attitudes to Mental Illness 2010′ and which is available here. I found the document slightly easier to read by starting at the back where the methodology is explained in detail. Random sampling took place and results were weighted according to the characteristics of the population in England in terms of age, sex and social grade (ethnicity data was included in the questionnaire). In the methodology section, the authors note that significant results imply that results are significant at the 5% level on the two-tailed t-test which presumably means that all of the sample data is normally distributed. I wasn’t clear on whether a correction for multiple comparisons was used. As there are a large number of comparisons being conducted between age groups, between genders and social grades as well as across surveys (i.e the previously conducted surveys) it would be expected that there would be false positives. So for instance with a Bonferroni correction, 10 comparisons would mean multiplying the resulting p values by 10 so that a p-value of 0.05 no longer remains significant at the 5% level. I wasn’t clear on this point. 1745 adults were selected using a random sampling method from areas across England (roughly 0.0035% of the English population). Results are displayed in graphs and also described in accompanying paragraphs. There is no interpretation of the data as far as I could see (e.g a discussion with reference to the literature).

Page 13 shows the sections of the survey. Scanning through the document there are 31 figures which I thought summarised the data effectively. There were a few points that I thought were interesting

In Figure 1 I didn’t notice any consistent trend in the data other than to say that the relationship between the lines remained fairly stable over time. The results here pertained to questions about social inclusion ranging from the nextdoor neighbour having a mental illness to a mental health institution being located within the neighbourhood. There was in fact a slight cross-over between the responses to the nextdoor neighbour and marriage questions over time seeming to indicate a possible trend to marriage becoming more acceptable to someone with a recognised mental illness.

Figure 2 was interesting because it showed significant age-group differences. Those over the age of 55 were more likely to judge those with a mental illness adversely i.e they shouldn’t hold public office. That is an interesting finding that would benefit from further follow-up with a qualitative study.

Figure 3 shows a significant difference between men and women in tolerance to people with a mental illness. Women were more likely to be tolerant in terms of their responses.

Figure 9 includes a graph of responses to inclusion in the community over time. Although there is an upward trend towards increasing acceptance in recent years the graph shows a lot of variability over time indicating many possibilites ranging from the question through to short-term factors that could influence responses.

Figure 16 was interesting as it most likely displays common perceptions of mental illness with schizophrenia being the most commonly identified mental illness with depression second. Perhaps here there could be clarification of what consitutes a mental illness. DSM-IV and ICD-10 contain a large number of diagnoses many of which may not feature prominently in the cultural ‘psyche’. It would be interesting to see the results of a survey in which familiarity with ICD-10/DSM-IV diagnoses is assessed in the general population as the public perception of mental illness may be biased towards a relatively small (although important) mental illnesses.

Figure 21 was encouraging in that 60% of the respondents samples believed that full recovery was possible for someone with a ‘mental health problems’ and there was confidence in the efficacy of medication and psychotherapy.

Figure 25 was  interesting as it showed that only 4% of the sample reported having a mental illness themselves although many of the respondents recognised the figure of 1 in 4 people having a mental illness. This implies either under-reporting or a biased sample.

Figure 30 shows that 50% of the sample would be uncomfortable talking to their employers about a mental illness. This means that there is some way to go in this area.

Figure 31 suggests that there has been a decrease in stigma.

This is a broad survey which can be usefully compared to previous surveys in this series. There are a number of encouraging findings in particular those relating to public perception of the possibility of recovery, integration into the community and efficacy of medication and psychotherapy. Areas which I thought were interesting were the self-reporting of mental illness as well as age-related perceptions of mental illness. These could be investigated further using a qualitative design.

Index

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The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

Review: Impairment of Instrumental ADL in Mild Cognitive Impairment

The paper reviewed here is ‘Impairment of Instrumental Activities of Daily Living in Patients with Mild Cognitive Impairment’ by Ahn and colleagues and freely available here. In the conclusion, the authors write that

The patients with MCI showed impairments in the ability to perform complex ADL in comparison to healthy controls. IADLS’s related to memory and frontal/executive functioning were particularly affected in MCI

The study was carried out in South Korea. The researchers have selected

  • 66 subjects with MCI (mean age 70.76 years)
  • 61 health controls (mean age 64 years)

Controls were recruited by advertisement whilst the recruitment method for the MCI group was unclear. The criterion used for MCI are clearly stipulated and include

  • Cognitive performance of 1.5 SD below the age and education norm in one or more of the following domains: memory, language, visuoconstruction, and frontal/executive function
  • Cognitive decline by self and/or informant reporting

The subjects were also administered a battery of neuropsychological tests including

  • Digit span forwards and backwards
  • Korean-Boston Naming Test
  • Rey-Kim Complex Figure TEST
  • Clock Drawing TEST
  • Contrasting Program, Go-no-go Test

amongst others. The study focused on the performance of MCI subjects on two measures of functioning – The Seoul Instrumental Activities of Daily Living (SIADL) and the Seoul Activities of Daily Living. About the SIADL, the researchers write that it assesses a number of functions and that

These include the ability to prepare a balanced meal, remember appointments, keep financial records, remember to take medication and so on

and is composed of 15 items. The primary research questions seems to be fairly straightforward – is there a difference between the MCI and control groups on the ADL’s? The researchers used a multivariate logistical regression analysis to investigate this relationship. They also wanted to find the optimal cut-off point for MCI and used a ROC curve for this purpose.

The MCI group scored significantly higher on the S-IADL than the control group (4.47 v 1.44) and this difference remained after controlling for variables including age. The S-IADL discriminated well between the control and MCI groups with a sensitivity and specificity of 82%. The researchers write that the

MCI patients showed significantly more impairment in the areas of ‘using the telephone’, ‘preparing meals’, ‘taking medication’, ‘managing belongings’, ‘keeping appointment’, ‘talking about recent events’, and ‘leisure/hobbies’ than normal elderly controls

Conclusions

Although these results are extremely encouraging I’m not sure if they generalise to the english version of the IADL and maybe another study with the English version needs to be undertaken. However the MCI group have a mean age of just over 70 years of age and again it would be unclear if this would generalise to a group in their early 60’s or late 50’s although the correction for age indicated that a significant difference between the groups remained.

There is an interesting question here which is about the precise nature of the relationship between the memory and executive components of the neuropsychological test battery and the performance on the IADL. Making a telephone call presumably involves a number of cognitive functions – motor cognition, working memory, episodic memory, auditory processing, attention and executive functions at the very least. Therefore if we could see which areas of the brain light up, during a telephone conversation (i.e fMRI) on the basis of the above we might expect to see the corresponding areas in the relevant order although it is never that simple. It might be expected that if some tasks require more cognitive functions they would be more susceptible to the effects of MCI – thus there might be a  hierarchy (although the individual tasks will vary in complexity).

More importantly from this study, this has implications for the workplace. If people have MCI and are working then this study suggests that it may interfere with a number of tasks around the workplace. If that is the case, then it would mean that assistive technologies may be useful. Furthermore the need for assistive technologies could be estimated from performance on a paper and pen test given the effective discrimination between the MCI and control groups (as MCI was assessed using the neuropsychological tests discussed above). It will be interesting to see if this study is replicated using an English version of the IADL.

 

Index

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Disclaimer

The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.

Review: Predicting Conversion to Dementia

The paper reviewed here is ‘Automated Detection of Brain Atrophy Patterns Based on MRI for the Prediction of Alzheimer’s Disease‘ by Plant and colleagues and freely available here. There are two things about this paper i’d like to mention. The first is that I didn’t completely understand it. I could probably get to grips with it in full but with a few weeks of extra reading around the topic and discussion (maybe). I understood enough of it to get the gist of it though. The paper has relevance to the practice of older adults psychiatry if such applications as described here become widely available which isn’t the case at the moment. It shouldn’t be suprising that this is a complicated paper to understand. After all, it’s by an international collaborative of multidisciplinary specialists in psychiatry, neuroimaging science, neuroradiology and computer science. Potentially therefore the audience lies in those disciplines. At the same time however, the audience would need to have knowledge traversing a number of disciplines and I suspect that there would be an extremely limited number of people who would be able to fully understand the paper with no prior preparation. Rather than meaning that this is a fairly esoteric subject which will end up with a number of other papers collecting dust however, it has potentially important clinical implications. Read

‘The extracted AD clusters were used as a search region to extract those brain areas that are predictive of conversion to AD within MCI subjects. The most predictive brain areas included the anterior cingulate gyrus and orbitofrontal cortex. The best prediction accuracy, which was cross-validated via train-and-test, was 75% for the prediction of the conversion from MCI to AD‘ (my underlining)

The essence of what the researchers were doing was identifying a group of subjects who were likely to develop Alzheimer’s Disease and then image their brains using an MRI scanner. They needed to compare these with two other groups – those that already had Alzheimer’s Disease and healthy controls. Then they used a number of sophisticated analysis techniques to discriminate between those with Mild Cognitive Impairment who went on to develop AD and those that did not. They identified individual brain regions that discriminated between the subjects and even give a predictive accuracy of 75%.

However the above is contingent on a number of assumptions which can be individually questioned.

Firstly what can be said about the subjects in the study. Well although some of the demographic details are given such as the average age, there are a number of other factors which aren’t clear from the article (there is an associated data article which I wasn’t able to access at the time of writing – perhaps the data might have been included there). So for instance, were there any concurrent medical illnesses, what were the numbers of years of education, blood pressure, concurrent medication and so on. I assume that the subject group was german given the approval by a Munich based ethics committee although this is implicit rather than explicit in the paper.

The next point is the bottom line. There are 9 people who convert from MCI to AD and 15 who don’t. Essentially that’s the basis for the comparisons. It’s a rather obvious and often repeated point but a larger sample size for comparison with a well characterised sample would be expected to lead to greater reliability as well as a better knowledge of generalisability.

The MRI scanner is 1.5T. The larger the field strength, the larger is the possible image resolution. The subjects’ images were normalised to an anatomical template. There were some additional steps which involved ‘masking’ the images to remove the CSF leaving just white and grey matter. I didn’t understand the process used to achieve this end. I’ve made this point elsewhere but where papers are highly technical it would be good for the research group to create a video and upload it to YouTube (for free) and link to it in the article so the interested reader can try and get up to speed quickly.

The authors then explain the data analysis. The section on feature selection was unclear to me and although people in the field might read it rather easily, I struggled to understand the entropy equation. Entropy as I understood it was a tendency for a gradient of energy to equilibriate after time or to substitute information for energy with similar results. So I wasn’t clear on why this term was being used here and it would benefit from an explanation as above. There are references to other papers but this phenomenon of linking in with other papers behind pay-walls is either costly in terms of resources or unhelpful (indeed it would mean there was a hidden cost in those papers were a fee is required) although is probably not an issue in university departments with appropriate subscriptions (even here however this is not the case as some of the referenced papers can be in obscure journals that are not included in a university’s subscriptions). So after reading a bit further on, i’m not sure I understand by what the authors refer to as feature detection although the term is usually used in neural network terminology to indicate patterns in information that are identified by a neural network architecture. If this were the case, then the authors might be referring to the algorithm for learning in the network when they talk about entropy although it is still unclear to me.

Moving onto clustering, the researchers write that they are using an approach to identify ‘highly discriminatory’ voxels and ‘remove noise’. Presumably they determine this by choosing conversion to AD as the outcome measure. However on scanning through this section I was unable to find the terms AD or MCI and instead it was an abstract generic mathematical discussion using language that is probably relevant to a highly specialised field of neuroimaging science but it doesn’t gel with the language used in the introduction.

I found that the explanation of classification was slightly easier to understand relating both to the AD/MCI categories with a little reading between the lines and also the explanation of analysis is consistent with neural network architectures.

With a limited amount of time to read the paper (a few hours), i’ve moved quickly through the training and visualisation sections. These sections quickly move into symbols. Now the problem with these symbols is that they make sense to someone in the very specialised field but are next to meaningless for people outside the field. Again here an animation or talk through video would be helpful. Symbols tend to be an abstract representation acquired once a shared understanding has been agreed – a useful shorthand for communication within the field. The authors might question why this should be communicated to someone outside of the field – after all one of the purposes of the method section is to communicate information to other groups for replication. I would argue that it’s necessary for clinician’s to understand the reasoning behind the ‘knowledge’ which they will be using to make clinical decisions when such approaches become more widespread.

SPM settings were given and then the authors report the method used for assessing white matter lesions.

In the results section, by the time I reached table 2 I had two thoughts

1. The results here seem impressive – high accuracy in the 90%’s, good sensitiviy and specificity

2. How did they get to this stage (which relates to the above discussion)

Again in Table 4 (AD v MCI)

1. These results are impressive and I recognise the brain regions involved

2. How did they get to this stage?

Unfortunately it’s easy to understand the significance of the results. Without fully understanding how the researchers got to this stage however I am left with three options

1. Make no decisions. Seems like a waste of 2 hours.

2. Reject the results. Seems a shame as a lot of work has gone into this and the researchers will undoubtedly be competent in their respective fields.

3. Accept the results. Pragmatism. Unfortunately if I didn’t understand the process by which the results were arrived at then I have to rely on ….. blind faith.

The same applies to Table 5.

Moving onto the discussion (I skipped the other bits that weren’t as interesting), the researchers write that

Using AD and HC as training data and MCI as test data, we achieved an accuracy of 50%–75% to predict conversion into AD

The authors also acknowledge the small sample size. In the above, the AD and control groups have been used to train the software thus making use of all subjects in the study and not just the 24 with MCI.

Conclusions

So there are some potentially useful results notably a complex multidisciplinary approach to discriminating people who convert from MCI to AD based on MRI and computer learning algorithms. Obviously if these results are valid then it would be nice to have this set-up available in a research setting with a focus on trialling interventions in the high-risk group. Papers like this are going to become increasingly commonplace. If a research group has an effective means for predicting who will convert from MCI to AD then it’s going to be very important and will most likely be repeatedly used and refined. Then there will come a point at which the clinicians will have to get up to speed with this approach. Only this runs into the problems described above. There has to come a point at which each step in the process is translated into an understandable format accessible to clinicians. If not then the clinician in the future will end up receiving a few numbers, without being able to argue about the underlying reasoning or being able to point out errors and exceptions. In that case, the clinician becomes deskilled in the decision-making process. This is the risk of using ever more sophisticated technology and research paradigms. The clinician still needs to be ‘connected’ to the increasingly complex underlying process.

There are a number of questions I still have

1. What are some of the other characteristics of the sample e.g comorbid illness?

2. When are papers going to be rated according to complexity?

3. When are complex papers going to link to videos explaining the methodology/results?

4. Will papers get more complex as even more disciplines become involved in large projects with multistage methods?

5. Who is the ideal audience for this paper and which people shouldn’t be reading this paper? (I think the results here are relevant to clinicians working in the field of dementia although perhaps it would be more relevant as the described approach becomes more accessible).

6. Would these results be more interesting if we had baseline MRI scans decades before the subjects developed MCI for comparison purposes?

7. If the reader has to take a leap of faith in accepting the results of a complex study then on what basis is this made. Is it a simple reduction to the ‘calibre’ of the researchers involved including the university that they work at, their title, previous publications and so on and if so is this a reliable approach?

Index

You can find an index of the site here. The page contains links to all of the articles in the blog in chronological order.

Twitter

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Podcast

You can listen to this post on Odiogo by clicking on this link (there may be a small delay between publishing of the blog article and the availability of the podcast).

TAWOP Channel

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Responses

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Disclaimer

The comments made here represent the opinions of the author and do not represent the profession or any body/organisation. The comments made here are not meant as a source of medical advice and those seeking medical advice are advised to consult with their own doctor. The author is not responsible for the contents of any external sites that are linked to in this blog.