Some neuroimaging caveats

In contrast to functional neuroimaging, structural imaging has not yielded robust intermediate phenotypes for schizophrenia, although a myriad of structural MRI studies have revealed whole brain and region-specific volume and other differences between patients with schizophrenia and controls.^15-17 As discussed elsewhere,¹⁸ variation in structural MRI measurements mav be attributable to artifacts other than the disease itself, physiological alterations in brain tissue (eg, tissue perfusion, body fat, or water content),¹⁹ or differences in image acquisition and analysis techniques. Alterations in body weight,²⁰ alcohol intake,²¹ steroid administration and hormonal status,²² can also change brain volume. Of critical importance in evaluating patients with chronic schizophrenia, medication can alter brain volume, sometimes rapidly; lithium carbonate reportedlyincreased cortical gray matter volume by 3% in patients with bipolar affective disorder after 4 weeks of treatment,²³ and multiple studies have reported basal ganglia volume change with neuroleptics over a duration of time intervals, including longitudinal studies of first-episode patients.^24,25 Furthermore, there has been a lack of reliability in reports of the heritability of brain structures, with volume reductions in unaffected siblings inconsistent across brain regions and different studies, and not consistently overlapping with genetic liability to schizophrenia, decreasing the utility of structural volumetric indices as intermediate phenotypes.^26,77 Increasingly, a growing number of research groups have used resting-state fMRI to map brain networks as well as diffusion tensor imaging (DTI) to investigate white matter abnormalities in patients with schizophrenia compared with controls. These modalities and investigations, however, are beyond the current focus of imaging genetics in the present review, but have been reviewed in depth elsewhere.^28-32 We would emphasize two caveats with respect to these approaches: (i) the analysis of resting fMRI patterns is very sensitive to variation in head motion and to the mental state of the subject, making problematic comparisons between ill and well samples³³; and (ii) DTI is based on highly derived data which are highly susceptible to many artifacts and biological events unrelated to white matter structure, rendering interpretation of the results also highly problematic.³⁴

In considering activation-based fMRI as applied to schizophrenia research there are methodological assumptions and limitations to recognize — the same limitations of fMRI in general — in its application to elucidating other CNS disorders or any cognitive function.³⁵ Firstly, brain mapping is predicated on the assumption of a modular organization of the brain, that there is a functional segregation with specialized and spatially separated modules, with interconnection of the entities and functional integration by distributed systems. Only according to this assumption can fMRI be then employed to reveal hierarchical decompositions of brain functional units. Another assumption is that a brain structure can be conceptualized as an information processing entity, with an input, a local processing capacity, and an output. Yet, the traditional cortical input-elaboration-output scheme, a correlate of the perception-cognition-action tripartite model, may be an oversimplification. In contributing to neuronal output, a change in balance between excitation and inhibition may predominate over a more straightforward hierarchical connectivity feedforward and feedback model.^36,37 Further, as for all hemodynamic-based modalities, fMRI measures a surrogate signal of brain function, but then justifies the assignment of a functional role to an ”active“ area, presuming the change in signal results from the change of activity of a neuronal population. Also, the blood oxygen level-dependent (BOLD) signal reflects neuronal mass activity that may limit its neurobiological inferences, even while being advantageous in some global instances. According to one estimate there are about 90 to 100 000 neurons under 1 mm² of cortical surface,³⁸ or as another metric, an unfiltered fMRI voxel contains 5.5 million neurons, and 2.2-5. 5x10¹⁰ synapses, in contrast to traditional microelectrode recordings. Lastly, for task-based fMRI neuroimaging studies, using a block design, a subtraction method is required to compare a task state with an investigator-designed control state, requiring a detailed task analysis to determine subtraction components and their interactions. Despite the above select critiques, fMRI remains the best tool at present for gaining insight into brain function, as many of these considerations are rendered relatively nonsys tematic because of the principle of every subject serving as his/her own control and the comparison of signals from one state to another. Thus, fMRI activation approaches allow for the ability to test discrete hypotheses about definable brain functions, especially the impact of genetic variation on these functions.

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