Transcranial Magnetic Stimulation for Schizophrenia.

People with schizophrenia typically experience auditory hallucinations or delusions during acute episodes. Although effective drug treatments are available, many have intractable symptoms that do not recover between acute episodes. One proposed alternative to drug treatments is transcranial magnetic stimulation (TMS). To date, many research trials to assess effectiveness of TMS for people with symptoms of schizophrenia have been conducted worldwide. However, there is a lack of consensus on whether TMS should be recommended to be adopted in routine clinical practice. We conducted a systematic review of the literature for all relevant randomized controlled trials (RCTs) comparing TMS with sham or standard treatment. Forty-one trials (1473 participants) survived eligibility criteria and had extractable data. We found significant differences in favor of temporoparietal TMS compared with sham TMS for global state (7 RCTs, n = 224, MD: -0.5, 95% CI: -0.76 to -0.23) and for positive symptoms measured on the Positive and Negative Syndrome Scale (5 RCTs, n = 127, MD: -6.09, 95% CI: -10.95 to -1.22). However, we also found that the quality of trial reporting was frequently suboptimal and the risks of bias were strong or unascertainable for many trial aspects; this led to many results being graded as very low-quality evidence. On that basis, we were unable to definitively support or refute the routine use of TMS in clinical practice. Future definitive trials of TMS with rigorous processes and high-quality reporting are needed.

Transcranial magnetic stimulation (TMS) for schizophrenia.

BACKGROUND: People with schizophrenia often experience symptoms which fail to fully respond to antipsychotic medication. Transcranial magnetic stimulation (TMS) has been proposed as a new treatment for people with schizophrenia, especially those who experience persistent auditory hallucinations. OBJECTIVES: To estimate the effects of TMS alone, compared with sham TMS or with 'standard management' and any other comparison interventions in reducing psychotic symptoms associated with schizophrenia. SEARCH METHODS: We searched the Cochrane Schizophrenia Group Trials Register (June 2006, June 2008, April 2013). This register is compiled by methodical searches of MEDLINE, EMBASE, BIOSIS, CINAHL, Dissertation abstracts, LILACS, PSYNDEX, PsycINFO, RUSSMED, and Sociofile, and is supplemented with handsearching of relevant journals and numerous conference proceedings. SELECTION CRITERIA: We included all randomised controlled trials recruiting at least five participants and comparing TMS with sham TMS or any other treatment for people with schizophrenia. DATA COLLECTION AND ANALYSIS: We extracted data independently. For dichotomous data we calculated relative risks (RRs) and their 95% confidence intervals (CIs). For continuous data, we calculated mean differences (MD) and 95% CI. We used a fixed-effect model. We assessed overall quality of the evidence using the GRADE approach. MAIN RESULTS: We included 41 studies with 1473 participants in the review. We found significant differences in favour of temporoparietal TMS compared to sham TMS for global state measured on the CGI scale (7 RCTs, n = 224, MD -0.5, 95% CI -0.76 to -0.23, very low-quality evidence) and positive symptoms measured on the PANSS scale (5 RCTs, n = 127, MD -6.09, 95% CI -10.95 to -1.22, very low-quality evidence). Participants experienced significantly more headaches in the temporoparietal TMS group (10 RCTs, n = 392, RR 2.65, 95% CI 1.56 to 4.50, very low-quality evidence). However, no more participants left the study early from the TMS group than from the sham group (very low-quality evidence). Cognitive state was assessed using 39 different measures, and all were equivocal (very low-quality evidence).We included only two trials which compared temporoparietal TMS with standard treatment. In both trials the participants received first- and second-generation antipsychotic medication in both treatment groups, therefore TMS was used an adjunctive therapy to medication. We found no significant differences in the number of participants that showed clinical improvement in global state (1 RCT, n = 100, RR 1.19, 95% CI 0.91 to 1.57) or left the study early (2 RCTs, n = 140, RR 0.33, 95% CI 0.08 to 1.46) (both very low-quality evidence). No studies reported on global state score, mental state, cognitive state and adverse effects.For prefrontal TMS compared to sham TMS, global state was measured on three different scales, all of which presented equivocal results (very low quality evidence). We could not pool data for mental state on the PANSS scale due to high heterogeneity. Cognitive state was assessed using 19 different measures, with 15/19 being equivocal (very low-quality evidence). Prefrontal TMS caused more headaches (6 RCTs, n = 164, RR 2.77, 95% CI 1.22 to 6.26, very low-quality evidence) but there was no difference in the number of participants leaving the study early (very low-quality evidence). No studies reported data for clinical improvement.We found a significant difference in favour of prefrontal theta burst stimulation TMS compared to sham TMS for mental state on the PANNS scale (3 RCTs, n = 108, MD -5.71, 95% CI -9.32 to -2.10, very low evidence). We found no difference for clinical improvement, cognitive state, number of headaches, and leaving the study early (very low-quality evidence).None of the included studies reported satisfaction with care. AUTHORS' CONCLUSIONS: Based on this review, there is insufficient evidence to support or refute the use of TMS to treat symptoms of schizophrenia. Although some evidence suggests that TMS, and in particular temporoparietal TMS, may improve certain symptoms (such as auditory hallucinations and positive symptoms of schizophrenia) compared to sham TMS, the results were not robust enough to be unequivocal across the assessment measures used. There was insufficient evidence to suggest any added benefit with TMS used as an adjunctive therapy to antipsychotic medication.The overall quality of evidence was graded as very low due to risk of bias, and this was accompanied by an imprecision in estimates due to the relatively small number of participants in the studies. Thus, consideration is required in improving the quality of trial processes, as well as the quality of reporting of ongoing and future TMS trials, so as to facilitate accurate future judgements in assessing risk of bias. Differences in TMS techniques in relation to stimulation intensity, stimulation length, brain areas stimulated and variations in the design of sham TMS all contributed to the heterogeneity of study findings and limited the interpretation and applicability of the results. In addition, the trials assessed their outcomes with a variety of scales, and usable data were limited. Therefore, to better evaluate the treatment effects of TMS in people with schizophrenia, we favour the use of standardised treatment protocols and outcome measures.

Magnetic resonance imaging in late-life depression: multimodal examination of network disruption.

CONTEXT: Disruption of frontal-subcortical and limbic networks is hypothesized to have a key role in late-life depression (LLD) and can be examined using magnetic resonance imaging (MRI) techniques. Gray matter can be examined using T1-weighted MRI, white matter using T2-weighted MRI and diffusion tensor imaging, and functional connectivity in resting-state networks using functional MRI. Although independent MRI studies have supported gray and white matter abnormalities in frontosubcortical and limbic networks and increased functional connectivity in the default-mode network in depression, no study has concurrently examined gray matter, white matter, and functional connectivity. OBJECTIVE: To examine whether results of different MRI techniques are complementary, multimodal MRI was used to compare gray matter, white matter, and resting-state networks between LLD and control groups. DESIGN: Cross-sectional, case-control, multimodal MRI analysis. SETTING: University research department. PARTICIPANTS: Thirty-six recovered participants with LLD (mean age, 71.8 years) and 25 control participants (mean age, 71.8 years). MAIN OUTCOME MEASURES: Gray matter was examined across the whole brain using voxel-based morphometry. Subcortical gray matter structures were also automatically segmented, and volumetric and shape analyses were performed. For white matter analysis, fractional anisotropy, axial diffusivity, and radial diffusivity values were examined using tract-based spatial statistics. For resting-state network analysis, correlation coefficients were compared using independent components analysis followed by dual regression. RESULTS: White matter integrity was widely reduced in LLD, without significant group differences in gray matter volumes or functional connectivity. CONCLUSIONS: The present work strongly supports the hypothesis that white matter abnormalities in frontal-subcortical and limbic networks play a key role in LLD even in the absence of changes in resting functional connectivity and gray matter. Factors that could contribute to the lack of significant differences in gray matter and functional connectivity measures, including current symptom severity, medication status, and age of participants with LLD, are discussed.

Does the Framingham Stroke Risk Profile predict white-matter changes in late-life depression?

BACKGROUND: Cardiovascular risk factors and diseases are important etiological factors in depression, particularly late-life depression. Brain changes associated with vascular disease and depression can be detected using magnetic resonance imaging. Using diffusion tensor imaging (DTI), we investigated whether the Framingham Stroke Risk Profile (FSRP), a well-validated risk prediction algorithm, is associated with changes in white-matter connectivity. We hypothesized that depressed participants would show reduced white-matter integrity with higher FSRP, and non-depressed controls (matched for mean vascular risk) would show minimal co-variance with white-matter changes. METHODS: Thirty-six participants with major depression (age 71.8 ± 7.7 years, mean FSRP 10.3 ± 7.6) and 25 controls (age 71.8 ± 7.3 years, mean FSRP 10.1 ± 7.7) were clinically interviewed and examined, followed by 60-direction DTI on a 3.0 Tesla scanner. Image analysis was performed using FSL tools (www.fmrib.ox.ac.uk/fsl) to assess the correlation between FSRP and fractional anisotropy (FA). Voxelwise statistical analysis of the FA data was carried out using Tract Based Spatial Statistics. The significance threshold for correlations was set at p < 0.05 using threshold-free cluster-enhancement. Partial correlation analysis investigated significant correlations in each group. RESULTS: Participants in the depressed group showed highly significant correlations between FSRP and FA within the body of corpus callosum (r = -0.520, p = 0.002), genu of corpus callosum (r = -0.468, p = 0.005), splenium of corpus callosum (r = -0.536, p = 0.001), and cortico-spinal tract (r = -0.473, p = 0.005). In controls, there was only one significant correlation in the body of corpus callosum (r = -0.473, p = 0.023). CONCLUSIONS: FSRP is associated with impairment in white-matter integrity in participants with depression; these results suggest support for the vascular depression hypothesis.

A meta-analysis of depression severity and cognitive function.

BACKGROUND: Studies examining the way in which cognitive impairment is associated with depression have produced inconsistent findings. Different severity of depressed mood across studies may account for such conflicting reports. However, inconsistent results have also been reported in relation to the specific association of depression severity with cognitive performance. METHODS: A meta-analysis was conducted to examine the relationship between severity of depression and cognitive function, using the correlation (Pearson's r) between depression severity scores and neuropsychological test performance. Individual meta-analyses were conducted for composite measures of cognitive functional domains (episodic memory, executive function, processing speed, semantic memory, and visuo-spatial memory). Analyses were also done across functional domains for timed and un-timed tests. RESULTS: Significant correlations between depression severity and cognitive performance were found in the domains of episodic memory, executive function, and processing speed, but not for semantic memory or visuo-spatial memory. For both timed and un-timed cognitive measures there were equally significant correlations with depression severity. LIMITATIONS: There were few studies meeting inclusion criteria in some cognitive domains, papers had to be excluded due to insufficient data reporting, and there are limitations associated with the cross-sectional design. CONCLUSIONS: The results suggest that previous inconsistent findings of the relationship between the severity of depression and cognitive function may be attributed to random variations and lack of power within studies.