Prospective Validation That Subgenual Connectivity Predicts Antidepressant Efficacy of Transcranial Magnetic Stimulation Sites

Graph analysis was used to study the effects of accelerated intermittent theta burst stimulation (aiTBS) on the brain's network topology in medication‐resistant depressed patients. Anatomical and resting‐state functional MRI (rs‐fMRI) was recorded at baseline and after sham and verum stimulation. Depression severity was assessed using the Hamilton Depression Rating Scale (HDRS). Using various graph measures, the different effects of sham and verum aiTBS were calculated. It was also investigated whether changes in graph measures were correlated to clinical responses. Furthermore, by correlating baseline graph measures with the changes in HDRS in terms of percentage, the potential of graph measures as biomarker was studied. Although no differences were observed between the effects of verum and sham stimulation on whole‐brain graph measures and changes in graph measures did not correlate with clinical response, the baseline values of clustering coefficient and global efficiency showed to be predictive of the clinical response to verum aiTBS. Nodal effects were found throughout the whole brain. The distribution of these effects could not be linked to the strength of the functional connectivity between the stimulation site and the node. This study showed that the effects of aiTBS on graph measures distribute beyond the actual stimulation site. However, additional research into the complex interactions between different areas in the brain is necessary to understand the effects of aiTBS in more detail.

Objective Holmes tremor is a debilitating movement disorder with limited treatment options. Lesions causing Holmes tremor can occur in multiple different brain locations, leaving the neuroanatomical substrate unclear. Here, we test whether lesion locations that cause Holmes tremor map to a connected brain circuit and whether this circuit might serve as a useful therapeutic target. Methods Case reports of Holmes tremor caused by focal brain lesions were identified through a systematic literature search. Connectivity between each lesion location and the rest of the brain was computed using resting state functional connectivity magnetic resonance imaging data from 1,000 healthy volunteers. Commonalities across lesion locations were identified. This Holmes tremor circuit was then compared to neurosurgical treatment targets and clinical efficacy. Results We identified 36 lesions causing Holmes tremor, which were scattered across multiple different brain regions. However, all lesion locations were connected to a common brain circuit with nodes in the red nucleus, thalamus, globus pallidus, and cerebellum. In cases with effective neurosurgical treatment, the treatment target was connected with the lesion location, indicating that a second hit to the same circuit might be beneficial. Commonly used deep brain stimulation targets such as the ventral intermediate nucleus and subthalamic nucleus fell outside our Holmes tremor circuit, whereas the globus pallidus target was close, consistent with published clinical response rates for these targets. Interpretation Lesions causing Holmes tremor are part of a single connected brain circuit that may serve as an improved therapeutic target. ANN NEUROL 2019;86:812–820

Neuroimaging biomarkers of treatment efficacy can be used to guide personalized treatment in major depressive disorder (MDD). Escitalopram is recommended as first‐line therapy for MDD and severe depression. An interesting hypothesis suggests that the reconfiguration of dynamic brain networks might provide important insights into antidepressant mechanisms. The present study assesses whether the spatiotemporal modulation across functional brain networks could serve as a predictor of effective antidepressant treatment with escitalopram. A total of 106 first‐episode, drug‐naïve patients and 109 healthy controls from three different multicenters underwent resting‐state functional magnetic resonance imaging. Patients were considered as responders if they had a reduction of at least 50% in Hamilton Rating Scale for Depression scores at endpoint (>2 weeks). Multilayer modularity framework was applied on the whole brain to construct features in relation to network dynamic characters that were used for multivariate pattern analysis. Linear soft‐threshold support vector machine models were used to separate responders from nonresponders. The permutation tests demonstrated the robustness of discrimination performances. The discriminative regions formed a spatially distributed pattern with anterior cingulate cortex (ACC) as the hub in the default mode subnetwork. Interestingly, a significantly larger module allegiance of ACC was also found in treatment responders compared to nonresponders, suggesting high interactivities of ACC to other regions may be beneficial for the recovery after treatment. Consistent results across multicenters confirmed that ACC could serve as a predictor of escitalopram monotherapy treatment outcome, implying strong likelihood of replication in the future.