Individualized precision targeting of dorsal attention and default mode networks with rTMS in traumatic brain injury-associated depression

At the group level, antidepressant efficacy of rTMS targets is inversely related to their normative connectivity with subgenual anterior cingulate cortex (sgACC). Individualized connectivity may yield better targets, particularly in patients with neuropsychiatric disorders who may have aberrant connectivity. However, sgACC connectivity shows poor test-retest reliability at the individual level. Individualized resting-state network mapping (RSNM) can reliably map inter-individual variability in brain network organization. Thus, we sought to identify individualized RSNM-based rTMS targets that reliably target the sgACC connectivity profile. We used RSNM to identify network-based rTMS targets in 10 healthy controls and 13 individuals with traumatic brain injury-associated depression (TBI-D). These RSNM targets were compared with consensus structural targets and targets based on individualized anti-correlation with a group-mean-derived sgACC region ( sgACC-derived targets ). The TBI-D cohort was also randomized to receive active (n = 9) or sham (n = 4) rTMS to RSNM targets with 20 daily sessions of sequential high-frequency left-sided stimulation and low-frequency right-sided stimulation. We found that the group-mean sgACC connectivity profile was reliably estimated by individualized correlation with default mode network (DMN) and anti-correlation with dorsal attention network (DAN). Individualized RSNM targets were thus identified based on DAN anti-correlation and DMN correlation. These RSNM targets showed greater test-retest reliability than sgACC-derived targets. Counterintuitively, anti-correlation with the group-mean sgACC connectivity profile was also stronger and more reliable for RSNM-derived targets than for sgACC-derived targets. Improvement in depression after RSNM-targeted rTMS was predicted by target anti-correlation with the portions of sgACC. Active treatment also led to increased connectivity within and between the stimulation sites, the sgACC, and the DMN. Overall, these results suggest that RSNM may enable reliable individualized rTMS targeting, although further research is needed to determine whether this personalized approach can improve clinical outcomes

Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease

Damage to specific brain circuits can cause specific neuropsychiatric symptoms. Therapeutic stimulation to these same circuits may modulate these symptoms. To determine whether these circuits converge, we studied depression severity after brain lesions (n = 461, five datasets), transcranial magnetic stimulation (n = 151, four datasets) and deep brain stimulation (n = 101, five datasets). Lesions and stimulation sites most associated with depression severity were connected to a similar brain circuit across all 14 datasets (P < 0.001). Circuits derived from lesions, deep brain stimulation and transcranial magnetic stimulation were similar (P < 0.0005), as were circuits derived from patients with major depression versus other diagnoses (P < 0.001). Connectivity to this circuit predicted out-of-sample antidepressant efficacy of transcranial magnetic stimulation and deep brain stimulation sites (P < 0.0001). In an independent analysis, 29 lesions and 95 stimulation sites converged on a distinct circuit for motor symptoms of Parkinson’s disease (P < 0.05). We conclude that lesions, transcranial magnetic stimulation and DBS converge on common brain circuitry that may represent improved neurostimulation targets for depression and other disorders

Wiley Interdiscip Rev Cogn Sci

Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.U01 MH 109991/MH/NIMH NIH HHSUnited States/RF1 MH116920/MH/NIMH NIH HHSUnited States/R01 MH120811/MH/NIMH NIH HHSUnited States/R01 MH111886/MH/NIMH NIH HHSUnited States/DP5 OD021352/CD/ODCDC CDC HHSUnited States/U01 MH109991/MH/NIMH NIH HHSUnited States/K01 MH121777/MH/NIMH NIH HHSUnited States/2021-10-18T00:00:00Z33470055PMC852143810454vault:3966

Investigation of brain networks for personalized rTMS in healthy subjects and patients with major depressive disorder: A translational study

Depression is a complex psychiatric disorder with emotional dysregulation at its core. The first line of treatment includes cognitive behaviour therapy and pharmacological antidepressants. However, up to one third of patients with depression fail to respond to these treatment interventions. The past decades have seen an increasing use of repetitive Transcranial Magnetic Stimulation (rTMS) in clinical studies, as an alternative treatment for depression. Several large-scale, multicentre randomized controlled trials have led the Food and Drugs Administration (FDA), USA to approve two rTMS protocols for clinical application in the treatment of depression - 10 Hz rTMS and intermittent Theta Burst Stimulation (iTBS). However, only 30-50% of patients receiving rTMS respond to the treatment. The large variability in response to rTMS likely stems from multiple reasons, one being the targeting method currently employed for delivering rTMS at the left dorsolateral prefrontal cortex (DLPFC). Previous functional connectivity studies have shown that stimulation at left DLPFC targets with larger negative correlation to the subgenual anterior cingulate cortex (sgACC) may result in greater therapeutic response than those with lower negative correlation. However, current use of rTMS ignores functional connectivity in choosing the left DLPFC target, thus largely discarding functional architectural differences of the brain across subjects. Furthermore, despite widespread clinical use of rTMS, the basic network mechanisms behind these rTMS protocols remain elusive. This work presents a novel personalization method of left DLPFC target selection based on their negative functional connectivity to the sgACC. The default mode network (DMN) is a large-scale brain network commonly involved in self-referential thought processing and plays an essential role in the pathophysiology of depression. I use the novel personalization method and identical study designs to delineate DMN mechanisms from a single session of 10 Hz rTMS and iTBS in healthy subjects. Arguably, an understanding of basic mechanisms of clinically relevant rTMS protocols in healthy subjects will help improve the current therapeutic effect of rTMS, and possibly expand the therapeutic role of rTMS. My work shows, for the first time, strong but different modulations of DMN connectivity by single personalized sessions of 10 Hz rTMS and iTBS. Such modulations can be predicted using the personality trait harm avoidance (HA). Given that initial results show that the method is robust and reproducible, its adaptation to patient cohorts is likely to result in improved therapeutic benefits. Therefore, the novel method of personalization is translated to clinical setting by using accelerated iTBS (aiTBS) in patients with depression. Additionally, a comparison is made between effects resulting from personalized and nonpersonalized (10-20 EEG system F3 position) aiTBS in patients with depression. By evaluating the DMN, and heart rate variability, I show precise modulatory effects of personalized aiTBS, which is not seen in the standard aiTBS group. The work presented here introduces an important method to reduce variability and increase precision in rTMS modulation by personalizing the left DLPFC target selection. Even though DMN and cardiac effects already point towards the advantage of personalization, the still preliminary analysis fails to show significant differences in treatment response. Lack of greater therapeutic benefits viii from personalized aiTBS in this ongoing study probably stems from a still limited sample size. In case personalization proves clinically advantageous to standard iTBS by the final sample size, this work can sediment the first step towards systems medicine in the field of psychiatry.2022-02-0

Personalized rTMS for Depression: A Review

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome

Identifying Functional Imaging Markers in Psychosis Using fMRI

Major types of psychotic disorders include schizophrenia (SCZ), bipolar disorder (BP) and schizoaffective disorder (SZA). These disorders have profound and overlapping symptoms with marked cognitive deficits, and their diagnosis relies on symptom clusters. The treatments for psychosis are usually focused on positive symptoms such as delusions and hallucinations. Although cognitive impairments underlie both positive and negative symptoms, functional brain imaging biomarkers that can reliably predict a patient\u27s cognitive deficits are still lacking. Therefore, this project used functional MRI to explore the feasibility of using functional connectivity (FC) to predict cognitive performance. A total of 207 subjects (BP: 79, SZ/SZA: 48, and HC: 80) with high functional MRI image (fMRI) quality (SNR\u3e 100, motion \u3c 0.3) were selected from the McLean MATRICS dataset. Subjects were divided into a discovery cohort (n=104) and an age, gender, and head motion matched validation cohort (n=103). The hypothesis was that FC could predict cognitive performance in the discovery cohort and that the prediction models could be generalized to the validation cohort. The connectomes for each subject were obtained by calculating the whole- brain connectivity using networks from the individualized functional parcellation as region of interests (ROIs). Models were trained to predict the 8 cognitive scores in the discovery cohort, respectively. The generalizability of these models was tested by applying these models to the validation cohort. The trained models were able to predict 6 out of 8 cognitive scores using a LOOCV procedure. Models for working memory, composite score and attention score could be generalized to the validation cohort. A total of 35 FC features were identified as important for predicting performance in these cognitive domains. Significant differences between patients and controls were found for 13 of these features when considered individually. In summary, this project has established a framework for biomarker discovery that may have clinical relevance for the diagnosis of psychosis early in the disease process by providing possible FC features that can be detected using fMRI and may help guide therapeutic interventions. The identified biomarkers also provide convergent evidence for network dysfunction in psychosis and suggest personalized treatment targets

Resting motor threshold and magnetic field output of the figure-of-8 and the double-cone coil

The use of the double-cone (DC) coil in transcranial magnetic stimulation (TMS) is promoted with the notion that the DC coil enables stimulation of deeper brain areas in contrast to conventional figure-of-8 (Fo8) coils. However, systematic comparisons of these two coil types with respect to the spatial distribution of the magnetic field output and also to the induced activity in superficial and deeper brain areas are limited. Resting motor thresholds of the left and right first dorsal interosseous (FDI) and tibialis anterior (TA) were determined with the DC and the Fo8 coil in 17 healthy subjects. Coils were orientated over the corresponding motor area in an angle of 45 degrees for the hand area with the handle pointing in posterior direction and in medio-lateral direction for the leg area. Physical measurements were done with an automatic gantry table using a Gaussmeter. Resting motor threshold was higher for the leg area in contrast to the hand area and for the Fo8 in contrast to the DC coil. Muscle by coil interaction was also significant providing higher differences between leg and hand area for the Fo8 (about 27%) in contrast to the DC coil (about 15%). Magnetic field strength was higher for the DC coil in contrast to the Fo8 coil. The DC coil produces a higher magnetic field with higher depth of penetration than the figure of eight coil

A Human Depression Circuit Derived From Focal Brain Lesions

Background: Focal brain lesions can lend insight into the causal neuroanatomical substrate of depression in the human brain. However, studies of lesion location have led to inconsistent results.Methods: Five independent datasets with different lesion etiologies and measures of postlesion depression were collated (N = 461). Each 3-dimensional lesion location was mapped to a common brain atlas. We used voxel lesion symptom mapping to test for associations between depression and lesion locations. Next, we computed the network of regions functionally connected to each lesion location using a large normative connectome dataset (N = 1000). We used these lesion network maps to test for associations between depression and connected brain circuits. Reproducibility was assessed using a rigorous leave-one-dataset-out validation. Finally, we tested whether lesion locations associated with depression fell within the same circuit as brain stimulation sites that were effective for improving poststroke depression.Results: Lesion locations associated with depression were highly heterogeneous, and no single brain region was consistently implicated. However, these same lesion locations mapped to a connected brain circuit, centered on the left dorsolateral prefrontal cortex. Results were robust to leave-one-dataset-out cross-validation. Finally, our depression circuit derived from brain lesions aligned with brain stimulation sites that were effective for improving poststroke depression.Conclusions: Lesion locations associated with depression fail to map to a specific brain region but do map to a specific brain circuit. This circuit may have prognostic utility in identifying patients at risk for poststroke depression and therapeutic utility in refining brain stimulation targets.</p