Somatosensory system deficits in schizophrenia revealed by MEG during a median-nerve oddball task.

Although impairments related to somatosensory perception are common in schizophrenia, they have rarely been examined in functional imaging studies. In the present study, magnetoencephalography (MEG) was used to identify neural networks that support attention to somatosensory stimuli in healthy adults and abnormalities in these networks in patient with schizophrenia. A median-nerve oddball task was used to probe attention to somatosensory stimuli, and an advanced, high-resolution MEG source-imaging method was applied to assess activity throughout the brain. In nineteen healthy subjects, attention-related activation was seen in a sensorimotor network involving primary somatosensory (S1), secondary somatosensory (S2), primary motor (M1), pre-motor (PMA), and paracentral lobule (PCL) areas. A frontal-parietal-temporal "attention network", containing dorsal- and ventral-lateral prefrontal cortex (DLPFC and VLPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), superior parietal lobule (SPL), inferior parietal lobule (IPL)/supramarginal gyrus (SMG), and temporal lobe areas, was also activated. Seventeen individuals with schizophrenia showed early attention-related hyperactivations in S1 and M1 but hypo-activation in S1, S2, M1, and PMA at later latency in the sensorimotor network. Within this attention network, hypoactivation was found in SPL, DLPFC, orbitofrontal cortex, and the dorsal aspect of ACC. Hyperactivation was seen in SMG/IPL, frontal pole, and the ventral aspect of ACC in patients. These findings link attention-related somatosensory deficits to dysfunction in both sensorimotor and frontal-parietal-temporal networks in schizophrenia.

Effect sizes and statistical testing in the determination of clinical significance in behavioral medicine research.

BACKGROUND: The interpretation of clinical significance continues to be an obstacle for researchers in behavioral medicine. PURPOSE: To review selected behavioral medicine research to critically examine the perception among investigators that behavioral effects on health are small based on common metrics of clinical significance. METHODS: Using quantitative findings from recent behavioral medicine research in medical and psychiatric journals, we explored results in terms of several statistical metrics to assess potential clinical significance: r coefficients, risk ratios, risk difference measures, and attributable risk. RESULTS: Translated into r coefficients, even established health predictors such as smoking, obesity, and fitness had only modest effects (rs =.03-.22), and the range of effect sizes were comparable with those based on psychological predictors including depression and stress-reactivity (rs =.06-.22). In contrast, effects for both classes of predictors were suggestive of clinical significance based on public health statistics. CONCLUSIONS: Our choice of statistics for defining "small" and "large" effect sizes affects the perceived importance of behavioral health findings. In the assessment of health outcomes with low incidence rates, effects expressed as correlations using even the most robust predictors will often appear small. In these instances, we challenge researchers to move beyond conventional data analysis approaches and to expand their clinical interpretation efforts by employing additional statistical methods favored in medicine and public health.