1 Boring Old Man

Neuroscience, Clinical Evidence, and the Future of Psychiatric Classification in DSM-5
^{by David J. Kupfer, M.D. and Darrel A. Regier, M.D., M.P.H.}
American Journal of Psychiatry 168:672-674, 2011.

^{In the initial stages of development of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders,we expected that some of the limitations of the current psychiatric diagnostic criteria and taxonomy would be mitigated by the integration of validators derived from scientific advances in the last few decades. Throughout the last 25 years of psychiatric research, findings from genetics, neuroimaging, cognitive science, and pathophysiology have yielded important insights into diagnosis and treatment approaches for some debilitating mental disorders, including depression, schizophrenia, and bipolar disorder. In A Research Agenda for DSM-V, we anticipated that these emerging diagnostic and treatment advances would impact the diagnosis and classification of mental disorders faster than what has actually occurred…}

Psychiatric Diagnosis in the Lab: How Far Off Are We?
Medscape News
by Jeffrey A. Lieberman, MD
09/28/2011

…we anticipated that this iteration of the DSM would incorporate biological markers and laboratory-based test results to augment the historical and phenomenologic criteria that traditionally are used to establish psychiatric diagnoses. Sadly, this has proved to be beyond the reach of the current level of evidence… In recent years, however, we have seen the emergence and refinement of a number of different technologies that I predict will, within our professional lifetimes and hopefully within the next 5 years, lead to the incorporation of laboratory-based tests for psychiatric diagnosis…

…likely to be implemented for psychiatric diagnosis is that of imaging techniques; here we’re talking about both nuclear medicine imaging with PET and MR imaging with either structural, spectroscopic, or functional imaging applications. These have been used in a variety of disorders. They yield clear differences between, diagnostic groups such as schizophrenia or depression on one hand and healthy volunteer controls or nonaffected individuals on the other. The problem is that the distributions of the values of the control vs patient groups still have too much overlap and are not sufficiently differentiated as to provide high enough positive predictive value at the individual patient or subject level. But I predict that it won’t be too long before these are refined, the results will become more robust, and these will contribute to a profile or augment the information that clinicians have to establish their diagnosis…

Brain circuitry model for mental illness will transform management, NIH mental health director says
British Medical Journal
by Caroline White
1 September 2011

The field of mental health is on the cusp of a revolution, which is set to transform the diagnosis and treatment of mental illness and reverse the lack of major progress made in curbing associated ill health and death over the past 100 years, the director of the US National Institute of Mental Health, has claimed. “We are at an extraordinary moment when the entire scientific foundation for mental health is shifting, with the 20th century discipline of psychiatry becoming the 21st century discipline of clinical neuroscience,” Thomas Insel said before a meeting on the challenges facing mental health research at the Royal Society in London on 31 August…

The seismic shift had been driven by what he described as three “revolutionary changes” in thinking, the first of which was that mental illness was increasingly being recognised as a disorder of brain circuitry, rather than as a chemical imbalance, thanks to neuroimaging techniques and the discovery of some key biomarkers. Secondly, mental ill health was now recognised as a developmental disorder for which early intervention was vital, said Professor Insel, highlighting US research showing that 50% of study participants had reported the onset of mental health problems by the age of 14, and 75% by the age of 24. “We are still stuck with getting to the problem very late. The future will be about understanding the trajectory of illness so that we can identify the first signs before it develops into psychosis,” he said…

^{The SPM5 software package [London, Wellcome Department of Imaging Neuroscience] was used to analyze the functional imaging data. Data were slice-time corrected for an interleaved bottom-up acquisition, adjusted for motion, and spatially normalized into a standard Montreal Neurological Institute [MNI] template. The image volume was then spatially smoothed with a 6-mm full width at half maximum Gaussian filter. Signal changes were modeled using the stimulation paradigm convolved with a canonical hemodynamic response function. Initial single-subject level analyses using a within-subject fixed-effects model were conducted to generate parametric maps for the three block conditions: neutral, fearful, and fixation. Results from the withinsubject analyses were then used for subsequent second-level random-effects models. Whole brain level t tests [two-sample and paired-sample] for the fearful > neutral contrast were conducted initially to guide subsequent region-of-interest analyses. Then a priori region-of-interest analyses were performed in regions of the amygdala, orbitofrontal cortex, and subgenual anterior cingulate cortex. Anatomic masks for the regions of interest were created using the automated anatomical labeling atlas in the Wake Forest University PickAtlas utility. Activations within the regions of interest had to survive a small-volume correction at a threshold of p<0.05. Single-subject percent signal changes were then calculated using the MarsBaR toolbox in SPM5. Functional masks for the amygdala were created using the voxels [five or more contiguous voxels] that showed a significant group-by-time interaction from the repeated-measures analysis of variance [ANOVA]. Masks for the orbitofrontal cortex and the subgenual anterior cingulate cortex were created using voxels from the baseline two-sample t tests. The percent signal change calculation allowed us not only to verify the results from the SPM5 analyses but also to account for other covariates in the analyses [baseline activation, gender, age, depression severity, and so on] using SAS, version 9.2 [SAS Institute, Inc., Cary, N.C.]. Thus, separate 2×2 [group-by-time] repeated-measures ANOVAs in SPM5 and a mixed linear model analysis of repeated measures with the Kenward-Roger correction applied to the unstructured covariance model in SAS were conducted to examine the differences in amygdala, orbitofrontal cortex, and subgenual anterior cingulate cortex activations between depressed adolescents and healthy comparison subjects for the fearful > neutral contrast over 8 weeks. Post hoc t tests in SPM5 and one-way ANOVAs or oneway analyses of covariance in SAS were conducted to evaluate the significant group-by-time interaction simple effects at baseline and at week 8, respectively. Additionally, mean changes in activation and the effect size of the change [Cohen’s d] from baseline to week 8 for each region were examined using mean contrasts.}