Hitting the Target of Image-Guided Psychiatry?

Transcranial magnetic stimulation for methamphetamine use disorder: A scoping review within the neurocircuitry model of addiction

The use of methamphetamine in the United States is increasing, contributing now to the "fourth wave" in the national opioid epidemic crisis. People who suffer from methamphetamine use disorder (MUD) have a higher risk of death. No pharmacological interventions are approved by the FDA and psychosocial interventions are only moderately effective. Transcranial Magnetic Stimulation (TMS) is a relatively novel FDA-cleared intervention for the treatment of Major Depressive Disorder (MDD) and other neuropsychiatric conditions. Several lines of research suggest that TMS could be useful for the treatment of addictive disorders, including MUD. We will review those published clinical trials that show potential effects on craving reduction of TMS when applied over the dorsolateral prefrontal cortex (DLPFC) also highlighting some limitations that affect their generalizability and applicability. We propose the use of the Koob and Volkow's neurocircuitry model of addiction as a frame to explain the brain effects of TMS in patients with MUD. We will finally discuss new venues that could lead to a more individualized and effective treatment of this complex disorder including the use of neuroimaging, the exploration of different areas of the brain such as the frontopolar cortex or the salience network and the use of biomarkers.

Targeting Symptom-Specific Networks With Transcranial Magnetic Stimulation

Increasing evidence suggests that the clinical effects of transcranial magnetic stimulation are target dependent. Within any given symptom, precise targeting of specific brain circuits may improve clinical outcomes. This principle can also be extended across symptoms-stimulation of different circuits may lead to different symptom-level outcomes. This may include targeting different symptoms within the same disorder (such as dysphoria vs. anxiety in patients with major depression) or targeting the same symptom across different disorders (such as primary major depression and depression secondary to stroke, traumatic brain injury, epilepsy, multiple sclerosis, or Parkinson's disease). Some of these symptom-specific changes may be desirable, while others may be undesirable. This review focuses on the conceptual framework through which symptom-specific target circuits may be identified, tested, and implemented.

Evaluating Robustness of Brain Stimulation Biomarkers for Depression: A Systematic Review of Magnetic Resonance Imaging and Electroencephalography Studies

Noninvasive brain stimulation (NIBS) treatments have gained considerable attention as potential therapeutic intervention for psychiatric disorders. The identification of reliable biomarkers for predicting clinical response to NIBS has been a major focus of research in recent years. Neuroimaging techniques, such as electroencephalography (EEG) and functional magnetic resonance imaging (MRI), have been used to identify potential biomarkers that could predict response to NIBS. However, identifying clinically actionable brain biomarkers requires robustness. In this systematic review, we aimed to summarize the current state of brain biomarker research for NIBS in depression, focusing only on well-powered studies (N ≥ 88) and/or studies that aimed at independently replicating previous findings, either successfully or unsuccessfully. A total of 220 studies were initially identified, of which 18 MRI studies and 18 EEG studies met the inclusion criteria. All focused on repetitive transcranial magnetic stimulation treatment in depression. After reviewing the included studies, we found the following MRI and EEG biomarkers to be most robust: 1) functional MRI-based functional connectivity between the dorsolateral prefrontal cortex and subgenual anterior cingulate cortex, 2) functional MRI-based network connectivity, 3) task-induced EEG frontal-midline theta, and 4) EEG individual alpha frequency. Future prospective studies should further investigate the clinical actionability of these specific EEG and MRI biomarkers to bring biomarkers closer to clinical reality.