The Static and dynamic functional connectivity characteristics of the left temporoparietal junction region in schizophrenia patients with auditory verbal hallucinations during low-frequency rTMS treatment

Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation Across Mental Disorders: A Systematic Review and Dose-Response Meta-Analysis

Importance

Noninvasive brain stimulation (NIBS) interventions have been shown to be efficacious in several mental disorders, but the optimal dose stimulation parameters for each disorder are unknown.

Objective

To define NIBS dose stimulation parameters associated with the greatest efficacy in symptom improvement across mental disorders.

Data Sources

Studies were drawn from an updated (to April 30, 2023) previous systematic review based on a search of PubMed, OVID, and Web of Knowledge.

Study Selection

Randomized clinical trials were selected that tested transcranial magnetic stimulation (TMS) or transcranial direct current stimulation (tDCS) for any mental disorder in adults aged 18 years or older.

Data Extraction and Synthesis

Two authors independently extracted the data. A 1-stage dose-response meta-analysis using a random-effects model was performed. Sensitivity analyses were conducted to test robustness of the findings. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline.

Main Outcomes and Measures

The main outcome was the near-maximal effective doses of total pulses received for TMS and total current dose in coulombs for tDCS.

Results

A total of 110 studies with 4820 participants (2659 men [61.4%]; mean [SD] age, 42.3 [8.8] years) were included. The following significant dose-response associations emerged with bell-shaped curves: (1) in schizophrenia, high-frequency (HF) TMS on the left dorsolateral prefrontal cortex (LDLPFC) for negative symptoms (χ2 = 9.35; df = 2; P = .009) and TMS on the left temporoparietal junction for resistant hallucinations (χ2 = 36.52; df = 2; P < .001); (2) in depression, HF-DLPFC TMS (χ2 = 14.49; df = 2; P < .001); (3) in treatment-resistant depression, LDLPFC tDCS (χ2 = 14.56; df = 2; P < .001); and (4) in substance use disorder, LDLPFC tDCS (χ2 = 33.63; df = 2; P < .001). The following significant dose-response associations emerged with plateaued or ascending curves: (1) in depression, low-frequency (LF) TMS on the right DLPFC (RDLPFC) with ascending curve (χ2 = 25.67; df = 2; P = .001); (2) for treatment-resistant depression, LF TMS on the bilateral DLPFC with ascending curve (χ2 = 5.86; df = 2; P = .004); (3) in obsessive-compulsive disorder, LF-RDLPFC TMS with ascending curve (χ2 = 20.65; df = 2; P < .001) and LF TMS on the orbitofrontal cortex with a plateaued curve (χ2 = 15.19; df = 2; P < .001); and (4) in posttraumatic stress disorder, LF-RDLPFC TMS with ascending curve (χ2 = 54.15; df = 2; P < .001). Sensitivity analyses confirmed the main findings.

Conclusions and Relevance

The study findings suggest that NIBS yields specific outcomes based on dose parameters across various mental disorders and brain regions. Clinicians should consider these dose parameters when prescribing NIBS. Additional research is needed to prospectively validate the findings in randomized, sham-controlled trials and explore how other parameters contribute to the observed dose-response association.

Using normative modeling to assess pharmacological treatment effect on brain state in patients with schizophrenia

Quantifying individual differences in neuroimaging metrics is attracting interest in clinical studies with mental disorders. Schizophrenia is diagnosed exclusively based on symptoms, and the biological heterogeneity makes it difficult to accurately assess pharmacological treatment effects on the brain state. Using the Cambridge Centre for Ageing and Neuroscience data set, we built normative models of brain states and mapped the deviations of the brain characteristics of each patient, to test whether deviations were related to symptoms, and further investigated the pharmacological treatment effect on deviation distributions. Specifically, we found that the patients can be divided into 2 groups: the normalized group had a normalization trend and milder symptoms at baseline, and the other group showed a more severe deviation trend. The baseline severity of the depression as well as the overall symptoms could predict the deviation of the static characteristics for the dorsal and ventral attention networks after treatment. In contrast, the positive symptoms could predict the deviations of the dynamic fluctuations for the default mode and dorsal attention networks after treatment. This work evaluates the effect of pharmacological treatment on static and dynamic brain states using an individualized approach, which may assist in understanding the heterogeneity of the illness pathology as well as the treatment response.