Distinct Symptom-Specific Treatment Targets for Circuit-Based Neuromodulation

Repetitive transcranial magnetic stimulation in Alzheimer's disease: effects on neural and synaptic rehabilitation

Alzheimer's disease is a neurodegenerative disease resulting from deficits in synaptic transmission and homeostasis. The Alzheimer's disease brain tends to be hyperexcitable and hypersynchronized, thereby causing neurodegeneration and ultimately disrupting the operational abilities in daily life, leaving patients incapacitated. Repetitive transcranial magnetic stimulation is a cost-effective, neuro-modulatory technique used for multiple neurological conditions. Over the past two decades, it has been widely used to predict cognitive decline; identify pathophysiological markers; promote neuroplasticity; and assess brain excitability, plasticity, and connectivity. It has also been applied to patients with dementia, because it can yield facilitatory effects on cognition and promote brain recovery after a neurological insult. However, its therapeutic effectiveness at the molecular and synaptic levels has not been elucidated because of a limited number of studies. This study aimed to characterize the neurobiological changes following repetitive transcranial magnetic stimulation treatment, evaluate its effects on synaptic plasticity, and identify the associated mechanisms. This review essentially focuses on changes in the pathology, amyloidogenesis, and clearance pathways, given that amyloid deposition is a major hypothesis in the pathogenesis of Alzheimer's disease. Apoptotic mechanisms associated with repetitive transcranial magnetic stimulation procedures and different pathways mediating gene transcription, which are closely related to the neural regeneration process, are also highlighted. Finally, we discuss the outcomes of animal studies in which neuroplasticity is modulated and assessed at the structural and functional levels by using repetitive transcranial magnetic stimulation, with the aim to highlight future directions for better clinical translations.

A comparative study of the dorsolateral prefrontal cortex targeting approaches for transcranial magnetic stimulation treatment: Insights from the healthy control data

Repetitive transcranial magnetic stimulation (rTMS) to the left dorsolateral prefrontal cortex (DLPFC) is an established treatment for medication-resistant depression. Several targeting methods for the left DLPFC have been proposed including identification with resting-state functional magnetic resonance imaging (rs-fMRI) neuronavigation, stimulus coordinates based on structural MRI, or electroencephalography (EEG) F3 site by Beam F3 method. To date, neuroanatomical and neurofunctional differences among those approaches have not been investigated on healthy subjects, which are structurally and functionally unaffected by psychiatric disorders. This study aimed to compare the mean location, its dispersion, and its functional connectivity with the subgenual cingulate cortex (SGC), which is known to be associated with the therapeutic outcome in depression, of various approaches to target the DLPFC in healthy subjects. Fifty-seven healthy subjects underwent MRI scans to identify the stimulation site based on their resting-state functional connectivity and were measured their head size for targeting with Beam F3 method. In addition, we included two fixed stimulus coordinates over the DLPFC in the analysis, as recommended in previous studies. From the results, the rs-fMRI method had, as expected, more dispersed target sites across subjects and the greatest anticorrelation with the SGC, reflecting the known fact that personalized neuronavigation yields the greatest antidepressant effect. In contrast, the targets located by the other methods were relatively close together with less dispersion, and did not differ in anticorrelation with the SGC, implying their limitation of the therapeutic efficacy and possible interchangeability of them.

Trajectories of suicidal ideation during rTMS for treatment-resistant depression

BACKGROUND

rTMS is a safe and effective intervention for treatment-resistant depression (TRD). However, there is limited data on its specific impact on suicidal ideation (SI), and the trajectory of SI over the treatment course.

OBJECTIVE

This open-label clinical trial investigated SI outcomes and trajectories in patients with TRD receiving low-frequency rTMS (LFR) to the right dorsolateral prefrontal cortex (DLPFC; N = 55).

METHODS

A latent class mixed-effect model was used to identify response trajectories for SI as well as core mood symptoms. Logistic regression analyses investigated risk factors associated with identified trajectories.

RESULTS

For each symptom domain, we identified two distinct trajectories during LFR, one tracking improvement (SI: n = 35, 60 %; mood: n = 29, 53 %) and the other tracking no improvement (SI: n = 20, 40 %; mood: n = 26, 47 %). Male sex, higher baseline anxiety, and higher baseline SI were risk factors for no improvement of SI; while higher baseline anxiety and benzodiazepine use were risk factors for no improvement of mood. Mediation analyses showed that anxiety was a risk factor for no improvement of SI and mood independent of benzodiazepine treatment.

CONCLUSIONS

This is the first study to investigate trajectories of response to LFR to the right DLPFC. SI and mood improved with LFR in most patients but the severity of anxiety symptoms was a factor of poor prognosis for both. Nuanced characterization of SI response to rTMS may lead to critical insights for individualized targeting strategies.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Efficacy of non-invasive brain stimulation and neuronavigation for major depressive disorder: a systematic review and meta-analysis

INTRODUCTION

Repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) are increasingly used for major depressive disorder (MDD). Most tDCS and rTMS studies target the left dorsolateral prefrontal cortex, either with or without neuronavigation. We examined the effect of rTMS and tDCS, and the added value of neuronavigation in the treatment of MDD.

METHODS

A search on PubMed, Embase, and Cochrane databases for rTMS or tDCS randomized controlled trials of MDD up to 1 February 2023, yielded 89 studies. We then performed meta-analyses comparing tDCS efficacy to non-neuronavigated rTMS, tDCS to neuronavigated rTMS, and neuronavigated rTMS to non-neuronavigated rTMS. We assessed the significance of the effect in subgroups and in the whole meta-analysis with a z-test and subgroup differences with a chi-square test.

RESULTS

We found small-to-medium effects of both tDCS and rTMS on MDD, with a slightly greater effect from rTMS. No significant difference was found between neuronavigation and non-neuronavigation.

CONCLUSION

Although both tDCS and rTMS are effective in treating MDD, many patients do not respond. Additionally, current neuronavigation methods are not significantly improving MDD treatment. It is therefore imperative to seek personalized methods for these interventions.

Critically Assessing the Unanswered Questions of How, Where, and When to Induce Plasticity in the Posttraumatic Stress Disorder Network with Transcranial Magnetic Stimulation

Extinction of traumatic memory, a primary treatment approach (termed exposure therapy) in post-traumatic stress disorder (PTSD), occurs through relearning and may be subserved at the molecular level by long-term potentiation (LTP) of relevant circuits. In parallel, repetitive transcranial magnetic stimulation (rTMS) is thought to work through LTP-like mechanisms and may provide a novel, safe, and effective treatment for PTSD. Our recent failed randomized controlled trial (1) emphasizes the necessity of correctly identifying cortical targets, directionality of TMS protocol, and role of memory activation. Here we provide a systematic review of TMS for PTSD to further identify how, where, and when TMS treatment should be delivered to alleviate PTSD symptoms. We conducted a systematic review of the literature searching for rTMS clinical trials involving PTSD patients and outcomes. We searched MEDLINE through October 25th, 2023 for "TMS and PTSD" and "transcranial magnetic stimulation and posttraumatic stress disorder." Thirty-one publications met our inclusion criteria (k=17 randomized controlled trials (RCTs), k=14 open label). RCT protocols were varied in TMS protocols, cortical TMS targets, and memory activation protocols. There was no clear superiority across protocols of low-frequency (k=5) vs. high-frequency protocols (k=6), or by stimulation location. Memory provocation or exposure protocols (k=7) appear to enhance response. Overall, TMS appears to be effective in treating PTSD symptoms across a variety of TMS frequencies, hemispheric target differences, and exposure protocols. Disparate protocols may be conceptually harmonized when viewed as potentiating proposed anxiolytic networks or suppressing anxiogenic networks.

Neuromodulatory transcranial magnetic stimulation (TMS) changes functional connectivity proportional to the electric-field induced by the TMS pulse

OBJECTIVE

Transcranial magnetic stimulation (TMS) can efficiently and robustly modulate synaptic plasticity, but little is known about how TMS affects functional connectivity (rs-fMRI). Accordingly, this project characterized TMS-induced rsFC changes in depressed patients who received 3 days of left prefrontal intermittent theta burst stimulation (iTBS).

METHODS

rs-fMRI was collected from 16 subjects before and after iTBS. Correlation matrices were constructed from the cleaned rs-fMRI data. Electric-field models were conducted and used to predict pre-post changes in rs-fMRI. Site by orientation heatmaps were created for vectors centered on the stimulation site and a control site (contralateral motor cortex).

RESULTS

For the stimulation site, there was a clear relationship between both site and coil orientation, and connectivity changes. As distance from the stimulation site increased, prediction accuracy decreased. Similarly, as eccentricity from the optimal orientation increased, prediction accuracy decreased. The systematic effects described above were not apparent in the heatmap centered on the control site.

CONCLUSIONS

These results suggest that rs-fMRI following iTBS changes systematically as a function of the distribution of electrical energy delivered from the TMS pulse, as represented by the e-field model.

SIGNIFICANCE

This finding lays the groundwork for future studies to individualize TMS targeting based on how predicted rs-fMRI changes might impact psychiatric symptoms.

Transcriptomic Evaluation of a Stress Vulnerability Network Using Single-Cell RNA Sequencing in Mouse Prefrontal Cortex

BACKGROUND

Increased vulnerability to stress is a major risk factor for several mood disorders, including major depressive disorder. Although cellular and molecular mechanisms associated with depressive behaviors following stress have been identified, little is known about the mechanisms that confer the vulnerability that predisposes individuals to future damage from chronic stress.

METHODS

We used multisite in vivo neurophysiology in freely behaving male and female C57BL/6 mice (n = 12) to measure electrical brain network activity previously identified as indicating a latent stress vulnerability brain state. We combined this neurophysiological approach with single-cell RNA sequencing of the prefrontal cortex to identify distinct transcriptomic differences between groups of mice with inherent high and low stress vulnerability.

RESULTS

We identified hundreds of differentially expressed genes (padjusted < .05) across 5 major cell types in animals with high and low stress vulnerability brain network activity. This unique analysis revealed that GABAergic (gamma-aminobutyric acidergic) neuron gene expression contributed most to the network activity of the stress vulnerability brain state. Upregulation of mitochondrial and metabolic pathways also distinguished high and low vulnerability brain states, especially in inhibitory neurons. Importantly, genes that were differentially regulated with vulnerability network activity significantly overlapped (above chance) with those identified by genome-wide association studies as having single nucleotide polymorphisms significantly associated with depression as well as genes more highly expressed in postmortem prefrontal cortex of patients with major depressive disorder.

CONCLUSIONS

This is the first study to identify cell types and genes involved in a latent stress vulnerability state in the brain.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Psychodynamically Informed Brain Stimulation: Building a Bridge from Brain to Mind

Since its inception, psychiatry has undergone several periods of radical identity transformation. Initially limited to psychotherapy alone, the advent of medications stimulated an era of biological psychiatry. For years, medications served as the mainstay of biological treatments, paralleled by a rise in treatment resistance. Brain stimulation therapies are psychiatry's newest arm of intervention and represent an area ripe for exploration. These techniques offer new hope to treatment-resistant patients, but in a manner often dissociated from psychoanalytic conceptualization and the practice of psychotherapy. There is growing interest in bridging this divide. In this article, we continue the efforts at interweaving what may seem to be disparate approaches through the topic of treatment resistance. This article aims to engage interventional psychiatrists in considering psychosocial dimensions of their treatments and to provide education for psychoanalytic clinicians on the history, mechanism of action, and applications of brain stimulation technologies.

A New Angle on Transcranial Magnetic Stimulation Coil Orientation: A Targeted Narrative Review

Transcranial magnetic stimulation (TMS) is used to treat several neuropsychiatric disorders including depression, where it is effective in approximately one half of patients for whom pharmacological approaches have failed. Treatment response is related to stimulation parameters such as the stimulation frequency, pattern, intensity, location, total number of pulses and sessions applied, and target brain network engagement. One critical but underexplored component of the stimulation procedure is the orientation or yaw angle of the commonly used figure-of-eight TMS coil, which is known to impact neuronal response to TMS. However, coil orientation has remained largely unchanged since TMS was first used to treat depression and continues to be based on motor cortex anatomy, which may not be optimal for the dorsolateral prefrontal cortex treatment site. In this targeted narrative review, we evaluate experimental, clinical, and computational evidence indicating that optimizing coil orientation may improve TMS treatment outcomes. The properties of the electric field induced by TMS, the changes to this field caused by the differing conductivities of head tissues, and the interaction between coil orientation and the underlying cortical anatomy are summarized. We describe evidence that the magnitude and site of cortical activation, surrogate markers of TMS dosing and brain network targeting considered central in clinical response to TMS, are influenced by coil orientation. We suggest that coil orientation should be considered when applying therapeutic TMS and propose several approaches to optimizing this potentially important treatment parameter.

Exploring Motor Network Connectivity in State-Dependent Transcranial Magnetic Stimulation: A Proof-of-Concept Study

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focus on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the Motor Network (MN) and how the MN state affects responses to the transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to the left M1 in eight subjects at rest, with simultaneous EEG. Motor-evoked potentials (MEPs) were measured from the right hand. The source space functional connectivity of the left M1 to the whole brain was assessed using the imaginary part of the phase locking value at the frequency of the sensorimotor μ-rhythm in a 1 s window before the pulse. Group-level connectivity revealed functional links between the left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At the single-subject level, this relation is more highly expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity-based NIBS.

Causal network localization of brain stimulation targets for trait anxiety

Transcranial magnetic stimulation (TMS) and deep brain stimulation (DBS) can treat some neuropsychiatric disorders, but there is no consensus approach for identifying new targets. We localized causal circuit-based targets for anxiety that converged across multiple natural experiments. Lesions (n=451) and TMS sites (n=111) that modify anxiety mapped to a common normative brain circuit (r=0.68, p=0.01). In an independent dataset (n=300), individualized TMS site connectivity to this circuit predicted anxiety change (p=0.02). Subthalamic DBS sites overlapping the circuit caused more anxiety (n=74, p=0.006), thus demonstrating a network-level effect, as the circuit was derived without any subthalamic sites. The circuit was specific to trait versus state anxiety in datasets that measured both (p=0.003). Broadly, this illustrates a pathway for discovering novel circuit-based targets across neuropsychiatric disorders.

Does theta burst stimulation have differential benefit for those with melancholic or non-melancholic depression?

BACKGROUND

To determine if theta burst stimulation (TBS) is of preferential benefit to those with melancholic or non-melancholic depression as an adjunctive treatment for treatment resistant depression (TRD).

METHODS

Fifty-two patients receiving TBS at a private psychiatric hospital participated in a naturalistic study. Four diagnostic strategies were used to assign melancholic versus non-melancholic depression subtype status. Depression symptoms were assessed at baseline, mid-treatment, and end of treatment using the Montgomery-Ǻsberg Depression Rating Scale - Self-Assessment (MADRS-S). Forty-one participants also completed the MADR-S at a six-week follow-up assessment.

RESULTS

We quantified poor correlations between the four study measures of melancholia; a finding suggesting that valid measurement of melancholia is likely to remain problematic. TBS led to significant reductions in depression symptoms from baseline to end of treatment, with this effect maintained at follow up. Response rates for the whole sample were 61.5 % at end of treatment and 53.7 % at follow-up, while remission rates were 34.6 % at end of treatment and 31.7 % at follow-up. Improvement rates as well as responder and remission rates were comparable for the melancholic and non-melancholic groups, irrespective of the diagnostic strategy used.

LIMITATIONS

The study was naturalistic (i.e., there being no control group, and concomitant medication changes were allowed), depression severity was assessed only by use of self-report measures, and the sample size was relatively small.

CONCLUSION

TBS appears to be non-specific, in that we failed to quantify any statistically significant differential benefit for those with melancholic compared to those with non-melancholic depression.

Bayesian Optimization of Neurostimulation (BOONStim)

BACKGROUND

Transcranial magnetic stimulation (TMS) treatment response is influenced by individual variability in brain structure and function. Sophisticated, user-friendly approaches, incorporating both established functional magnetic resonance imaging (fMRI) and TMS simulation tools, to identify TMS targets are needed.

OBJECTIVE

The current study presents the development and validation of the Bayesian Optimization of Neuro-Stimulation (BOONStim) pipeline.

METHODS

BOONStim uses Bayesian optimization for individualized TMS targeting, automating interoperability between surface-based fMRI analytic tools and TMS electric field modeling. Bayesian optimization performance was evaluated in a sample dataset (N=10) using standard circular and functional connectivity-defined targets, and compared to grid optimization.

RESULTS

Bayesian optimization converged to similar levels of total electric field stimulation across targets in under 30 iterations, converging within a 5% error of the maxima detected by grid optimization, and requiring less time.

CONCLUSIONS

BOONStim is a scalable and configurable user-friendly pipeline for individualized TMS targeting with quick turnaround.

A potential neuromodulation target for PTSD in Veterans derived from focal brain lesions

Neuromodulation trials for PTSD have yielded mixed results, and the optimal neuroanatomical target remains unclear. We analyzed three datasets to study brain circuitry causally linked to PTSD in military Veterans. After penetrating traumatic brain injury (n=193), lesions that reduced probability of PTSD were preferentially connected to a circuit including the medial prefrontal cortex (mPFC), amygdala, and anterolateral temporal lobe (cross-validation p=0.01). In Veterans without lesions (n=180), PTSD was specifically associated with connectivity within this circuit (p<0.01). Connectivity change within this circuit correlated with PTSD improvement after transcranial magnetic stimulation (TMS) (n=20) (p<0.01), even though the circuit was not directly targeted. Finally, we directly targeted this circuit with fMRI-guided accelerated TMS, leading to rapid resolution of symptoms in a patient with severe lifelong PTSD. All results were independent of depression severity. This lesion-based PTSD circuit may serve as a neuromodulation target for Veterans with PTSD.

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.

Accelerated Theta Burst Stimulation: Safety, Efficacy, and Future Advancements

Theta burst stimulation (TBS) is a noninvasive brain stimulation technique that can be used to modulate neural networks underlying psychiatric and neurological disorders. TBS can be delivered intermittently or continuously. The conventional intermittent TBS protocol is approved by the U.S. Food and Drug Administration to treat otherwise treatment-resistant depression, but the 6-week duration limits the applicability of this therapy. Accelerated TBS protocols present an opportunity to deliver higher pulse doses in shorter periods of time, thus resulting in faster and potentially more clinically effective treatment. However, the acceleration of TBS delivery raises questions regarding the relative safety, efficacy, and durability compared with conventional TBS protocols. In this review paper, we present the data from accelerated TBS trials to date that support the safety and effectiveness of accelerated protocols while acknowledging the need for more durability data. We discuss the stimulation parameters that seem to be important for the efficacy of accelerated TBS protocols and possible avenues for further optimization.

Personalized and Circuit-Based Transcranial Magnetic Stimulation: Evidence, Controversies, and Opportunities

The development of neuroimaging methodologies to map brain connectivity has transformed our understanding of psychiatric disorders, the distributed effects of brain stimulation, and how transcranial magnetic stimulation can be best employed to target and ameliorate psychiatric symptoms. In parallel, neuroimaging research has revealed that higher-order brain regions such as the prefrontal cortex, which represent the most common therapeutic brain stimulation targets for psychiatric disorders, show some of the highest levels of interindividual variation in brain connectivity. These findings provide the rationale for personalized target site selection based on person-specific brain network architecture. Recent advances have made it possible to determine reproducible personalized targets with millimeter precision in clinically tractable acquisition times. These advances enable the potential advantages of spatially personalized transcranial magnetic stimulation targeting to be evaluated and translated to basic and clinical applications. In this review, we outline the motivation for target site personalization, preliminary support (mostly in depression), convergent evidence from other brain stimulation modalities, and generalizability beyond depression and the prefrontal cortex. We end by detailing methodological recommendations, controversies, and notable alternatives. Overall, while this research area appears highly promising, the value of personalized targeting remains unclear, and dedicated large prospective randomized clinical trials using validated methodology are critical.

Individualized brain mapping for navigated neuromodulation

ABSTRACT

The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

ENIGMA's simple seven: Recommendations to enhance the reproducibility of resting-state fMRI in traumatic brain injury

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

Enhancing cognitive control with transcranial magnetic stimulation in subject-specific frontoparietal networks

BACKGROUND

Cognitive control processes, including those involving frontoparietal networks, are highly variable between individuals, posing challenges to basic and clinical sciences. While distinct frontoparietal networks have been associated with specific cognitive control functions such as switching, inhibition, and working memory updating functions, there have been few basic tests of the role of these networks at the individual level.

METHODS

To examine the role of cognitive control at the individual level, we conducted a within-subject excitatory transcranial magnetic stimulation (TMS) study in 19 healthy individuals that targeted intrinsic ("resting") frontoparietal networks. Person-specific intrinsic networks were identified with resting state functional magnetic resonance imaging scans to determine TMS targets. The participants performed three cognitive control tasks: an adapted Navon figure-ground task (requiring set switching), n-back (working memory), and Stroop color-word (inhibition).

OBJECTIVE

Hypothesis: We predicted that stimulating a network associated with externally oriented control [the "FPCN-B" (fronto-parietal control network)] would improve performance on the set switching and working memory task relative to a network associated with attention (the Dorsal Attention Network, DAN) and cranial vertex in a full within-subjects crossover design.

RESULTS

We found that set switching performance was enhanced by FPCN-B stimulation along with some evidence of enhancement in the higher-demand n-back conditions.

CONCLUSION

Higher task demands or proactive control might be a distinguishing role of the FPCN-B, and personalized intrinsic network targeting is feasible in TMS designs.

Continuous theta burst stimulation to dorsomedial prefrontal cortex in young adults with depression: Changes in resting frontostriatal functional connectivity relevant to positive mood

Depression is associated with diminished positive affect (PA), postulated to reflect frontostriatal reward circuitry disruptions. Depression has consistently been associated with higher dorsomedial prefrontal cortex (dmPFC) activation, a region that regulates PA through ventral striatum (VS) connections. Low PA in depression may reflect dmPFC's aberrant functional connectivity (FC) with the VS. To test this, we applied theta burst stimulation (TBS) to dmPFC in 29 adults with depression (79% female, Mage = 21.4, SD = 2.04). Using a randomized, counterbalanced design, we administered 3 types of TBS at different sessions: intermittent (iTBS; potentiating), continuous (cTBS; depotentiating), and sham TBS (control). We used neuronavigation to target personalized dmPFC targets based on VS-dmPFC FC. PA and negative affect (NA), and resting-state fMRI were collected pre- and post-TBS. We found no changes in PA or NA with time (pre/post), condition (iTBS, cTBS, sham), or their interaction. Functional connectivity (FC) between the nucleus accumbens and dmPFC showed a significant condition (cTBS, iTBS, and sham) by time (pre-vs. post-TBS) interaction, and post-hoc testing showed decreased pre-to post-TBS for cTBS but not iTBS or sham. For cTBS only, reduced FC pre/post stimulation was associated with increased PA (but not NA). Our findings lend support to the proposed mechanistic model of aberrant FC between the dmPFC and VS in depression and suggest a way forward for treating depression in young adults. Future studies need to evaluate multi-session TBS to test clinical effects.

Individualized, connectome-based, non-invasive stimulation of OCD deep-brain targets: A proof-of-concept

Treatment-resistant obsessive-compulsive disorder (OCD) generally improves with deep-brain stimulation (DBS), thought to modulate neural activity at both the implantation site and in connected brain regions. However, its invasive nature, side-effects, and lack of customization, make non-invasive treatments preferable. Harnessing the established remote effects of cortical transcranial magnetic stimulation (TMS), connectivity-based approaches have emerged for depression that aim at influencing distant regions connected to the stimulation site. We here investigated whether effective OCD DBS targets (here subthalamic nucleus [STN] and nucleus accumbens [NAc]) could be modulated non-invasively with TMS. In a proof-of-concept study with nine healthy individuals, we used 7T magnetic resonance imaging (MRI) and probabilistic tractography to reconstruct the fiber tracts traversing manually segmented STN/NAc. Two TMS targets were individually selected based on the strength of their structural connectivity to either the STN, or both the STN and NAc. In a sham-controlled, within-subject cross-over design, TMS was administered over the personalized targets, located around the precentral and middle frontal gyrus. Resting-state functional 3T MRI was acquired before, and at 5 and 25 min after stimulation to investigate TMS-induced changes in the functional connectivity of the STN and NAc with other regions of the brain. Static and dynamic seed-to-voxel correlation analyses were conducted. TMS over both targets was able to modulate the functional connectivity of the STN and NAc, engaging both overlapping and distinct regions, and unfolding following different temporal dynamics. Given the relevance of the engaged connected regions to OCD pathology, we argue that a personalized, connectivity-based procedure is worth investigating as potential treatment for refractory OCD.

Transcriptomic Similarity Informs Neuromorphic Deviations in Depression Biotypes

BACKGROUND

Major depressive disorder (MDD) is complicated by population heterogeneity, motivating the investigation of biotypes through imaging-derived phenotypes. However, neuromorphic heterogeneity in MDD remains unclear, and how the correlated gene expression (CGE) connectome constrains these neuromorphic anomalies in MDD biotypes has not yet been studied.

METHODS

Here, we related cortical thickness deviations in MDD biotypes to a pattern of CGE connectome. Cortical thickness was estimated from 3-dimensional T1-weighted magnetic resonance images in 2 independent cohorts (discovery cohort: N = 425; replication cohort: N = 217). The transcriptional activity was measured according to Allen Human Brain Atlas. A density peak-based clustering algorithm was used to identify MDD biotypes.

RESULTS

We found that patients with MDD were clustered into 2 replicated biotypes based on single-patient regional deviations from healthy control participants across 2 datasets. Biotype 1 mainly exhibited cortical thinning across the brain, whereas biotype 2 mainly showed cortical thickening in the brain. Using brainwide gene expression data, we found that deviations of transcriptionally connected neighbors predicted regional deviation for both biotypes. Furthermore, putative CGE-informed epicenters of biotype 1 were concentrated on the cognitive control circuit, whereas biotype 2 epicenters were located in the social perception circuit. The patterns of epicenter likelihood were separately associated with depression- and anxiety-response maps, suggesting that epicenters of MDD biotypes may be associated with clinical efficacies.

CONCLUSIONS

Our findings linked the CGE connectome and neuromorphic deviations to identify distinct epicenters in MDD biotypes, providing insight into how microscale gene expressions informed MDD biotypes.

Identifying a stable and generalizable factor structure of major depressive disorder across three large longitudinal cohorts

The Patient Health Questionnaire 9 (PHQ-9) is the current standard outpatient screening tool for measuring and tracking the nine symptoms of major depressive disorder (MDD). While the PHQ-9 was originally conceptualized as a unidimensional measure, it has become clear that MDD is not a monolithic construct, as evidenced by high comorbidities with other theoretically distinct diagnoses and common symptom overlap between depression and other diagnoses. Therefore, identifying reliable and temporally stable subfactors of depressive symptoms could allow research and care to be tailored to different depression phenotypes. This study improved on previous factor analysis studies of the PHQ-9 by leveraging samples that were clinical (participants with depression only), large (N = 1483 depressed individuals in total), longitudinal (up to 5 years), and from three diverse (matching racial distribution of the United States) datasets. By refraining from assuming the number of factors or item loadings a priori, and thus utilizing a solely data-driven approach, we identified a ranked list of best-fitting models, with the parsimonious one achieving good model fit across studies at most timepoints (average TLI >= 0.90). This model categorizes the PHQ-9 items into four factors: (1) Affective (Anhedonia + Depressed Mood), (2) Somatic (Sleep + Fatigue + Appetite), (3) Internalizing (Worth/Guilt + Suicidality), (4) Sensorimotor (Concentration + Psychomotor), which may be used to further precision psychiatry by testing factor-specific interventions in research and clinical settings.

Personalized connectivity-based network targeting model of transcranial magnetic stimulation for treatment of psychiatric disorders: computational feasibility and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) holds promise for treating psychiatric disorders; however, the variability in treatment efficacy among individuals underscores the need for further improvement. Growing evidence has shown that TMS induces a broad network modulatory effect, and its effectiveness may rely on accurate modulation of the pathological network specific to each disorder. Therefore, determining the optimal TMS coil setting that will engage the functional pathway delivering the stimulation is crucial. Compared to group-averaged functional connectivity (FC), individual FC provides specific information about a person's brain functional architecture, offering the potential for more accurate network targeting for personalized TMS. However, the low signal-to-noise ratio (SNR) of FC poses a challenge when utilizing individual resting-state FC. To overcome this challenge, the proposed solutions include increasing the scan duration and employing the cluster method to enhance the stability of FC. This study aimed to evaluate the stability of a personalized FC-based network targeting model in individuals with major depressive disorder or schizophrenia with auditory verbal hallucinations. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, we assessed the model's stability. We employed longer scan durations and cluster methodologies to improve the precision in identifying optimal individual sites. Our findings demonstrate that a scan duration of 28 minutes and the utilization of the cluster method achieved stable identification of individual sites, as evidenced by the intraindividual distance falling below the ~1cm spatial resolution of TMS. The current model provides a feasible approach to obtaining stable personalized TMS targets from the scalp, offering a more accurate method of TMS targeting in clinical applications.

Elevating the field for applying neuroimaging to individual patients in psychiatry

Although neuroimaging has been widely applied in psychiatry, much of the exuberance in decades past has been tempered by failed replications and a lack of definitive evidence to support the utility of imaging to inform clinical decisions. There are multiple promising ways forward to demonstrate the relevance of neuroimaging for psychiatry at the individual patient level. Ultra-high field magnetic resonance imaging is developing as a sensitive measure of neurometabolic processes of particular relevance that holds promise as a new way to characterize patient abnormalities as well as variability in response to treatment. Neuroimaging may also be particularly suited to the science of brain stimulation interventions in psychiatry given that imaging can both inform brain targeting as well as measure changes in brain circuit communication as a function of how effectively interventions improve symptoms. We argue that a greater focus on individual patient imaging data will pave the way to stronger relevance to clinical care in psychiatry. We also stress the importance of using imaging in symptom-relevant experimental manipulations and how relevance will be best demonstrated by pairing imaging with differential treatment prediction and outcome measurement. The priorities for using brain imaging to inform psychiatry may be shifting, which compels the field to solidify clinical relevance for individual patients over exploratory associations and biomarkers that ultimately fail to replicate.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

Dimensional and Categorical Solutions to Parsing Depression Heterogeneity in a Large Single-Site Sample

BACKGROUND

Recent studies have reported significant advances in modeling the biological basis of heterogeneity in major depressive disorder, but investigators have also identified important technical challenges, including scanner-related artifacts, a propensity for multivariate models to overfit, and a need for larger samples with more extensive clinical phenotyping. The goals of the current study were to evaluate dimensional and categorical solutions to parsing heterogeneity in depression that are stable and generalizable in a large, single-site sample.

METHODS

We used regularized canonical correlation analysis to identify data-driven brain-behavior dimensions that explain individual differences in depression symptom domains in a large, single-site dataset comprising clinical assessments and resting-state functional magnetic resonance imaging data for 328 patients with major depressive disorder and 461 healthy control participants. We examined the stability of clinical loadings and model performance in held-out data. Finally, hierarchical clustering on these dimensions was used to identify categorical depression subtypes.

RESULTS

The optimal regularized canonical correlation analysis model yielded 3 robust and generalizable brain-behavior dimensions that explained individual differences in depressed mood and anxiety, anhedonia, and insomnia. Hierarchical clustering identified 4 depression subtypes, each with distinct clinical symptom profiles, abnormal resting-state functional connectivity patterns, and antidepressant responsiveness to repetitive transcranial magnetic stimulation.

CONCLUSIONS

Our results define dimensional and categorical solutions to parsing neurobiological heterogeneity in major depressive disorder that are stable, generalizable, and capable of predicting treatment outcomes, each with distinct advantages in different contexts. They also provide additional evidence that regularized canonical correlation analysis and hierarchical clustering are effective tools for investigating associations between functional connectivity and clinical symptoms.

A Retrospective Case-Control Study on the Differences in the Effectiveness of Theta-Burst Stimulation Therapy for Depression with and without Antidepressant Medication

Transcranial magnetic stimulation (TMS) therapy has few side effects and comparable therapeutic effects to antidepressant treatment, but few studies have introduced TMS therapy as an initial treatment for MDD. The objective of this study was to retrospectively compare the clinical outcomes between 50 MDD patients without antidepressants (i.e., TMS monotherapy) and 50 MDD patients with antidepressants plus TMS therapy, matched for age, sex, and depression severity. The presence or absence of antidepressant therapy in first-line treatment was determined via a detailed interview by psychiatrists. The study design was a retrospective observational case-control study using the TMS registry data. The key inclusion criteria were adult patients who met the diagnosis of MDD and received 20-30 sessions of intermittent theta-burst stimulation (iTBS) therapy to the left dorsolateral prefrontal cortex (DLPFC). In this study, the Montgomery-Åsberg Depression Rating Scale (MADRS) was used as the primary outcome measure. No significant group differences existed in the baseline MADRS total score between the unmedicated and medicated patient groups. Following TMS therapy, no significant group differences in response rate, remission rate, or relative total score change in the MADRS were observed. The main limitations were the retrospective design and the use of registry data as a source. Our findings suggest that TMS monotherapy may be as effective as TMS add-on therapy to antidepressants when used as the first-line therapy for MDD, but randomized controlled trials are needed.

Targeting suicidal ideation in major depressive disorder with MRI-navigated Stanford accelerated intelligent neuromodulation therapy

High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.

The future of brain circuit-targeted therapeutics

The principle of targeting brain circuits has drawn increasing attention with the growth of brain stimulation treatments such as transcranial magnetic stimulation (TMS), deep brain stimulation (DBS), and focused ultrasound (FUS). Each of these techniques can effectively treat different neuropsychiatric disorders, but treating any given disorder depends on choosing the right treatment target. Here, we propose a three-phase framework for identifying and modulating these targets. There are multiple approaches to identifying a target, including correlative neuroimaging, retrospective optimization based on existing stimulation sites, and lesion localization. These techniques can then be optimized using personalized neuroimaging, physiological monitoring, and engagement of a specific brain state using pharmacological or psychological interventions. Finally, a specific stimulation modality or combination of modalities can be chosen after considering the advantages and tradeoffs of each. While there is preliminary literature to support different components of this framework, there are still many unanswered questions. This presents an opportunity for the future growth of research and clinical care in brain circuit therapeutics.

Mechanisms of Action of TMS in the Treatment of Depression

Transcranial magnetic stimulation (TMS) is entering increasingly widespread use in treating depression. The most common stimulation target, in the dorsolateral prefrontal cortex (DLPFC), emerged from early neuroimaging studies in depression. Recently, more rigorous casual methods have revealed whole-brain target networks and anti-networks based on the effects of focal brain lesions and focal brain stimulation on depression symptoms. Symptom improvement during therapeutic DLPFC-TMS appears to involve directional changes in signaling between the DLPFC, subgenual and dorsal anterior cingulate cortex, and salience-network regions. However, different networks may be involved in the therapeutic mechanisms for other TMS targets in depression, such as dorsomedial prefrontal cortex or orbitofrontal cortex. The durability of therapeutic effects for TMS involves synaptic neuroplasticity, and specifically may depend upon dopamine acting at the D1 receptor family, as well as NMDA-receptor-dependent synaptic plasticity mechanisms. Although TMS protocols are classically considered 'excitatory' or 'inhibitory', the actual effects in individuals appear quite variable, and might be better understood at the level of populations of synapses rather than individual synapses. Synaptic meta-plasticity may provide a built-in protective mechanism to avoid runaway facilitation or inhibition during treatment, and may account for the relatively small number of patients who worsen rather than improve with TMS. From an ethological perspective, the antidepressant effects of TMS may involve promoting a whole-brain attractor state associated with foraging/hunting behaviors, centered on the rostrolateral periaqueductal gray and salience network, and suppressing an attractor state associated with passive threat defense, centered on the ventrolateral periaqueductal gray and default-mode network.

Systematic cross-species comparison of prefrontal cortex functional networks targeted via Transcranial Magnetic Stimulation

Transcranial Magnetic Stimulation (TMS) is a non-invasive brain stimulation method that safely modulates neural activity in vivo. Its precision in targeting specific brain networks makes TMS invaluable in diverse clinical applications. For example, TMS is used to treat depression by targeting prefrontal brain networks and their connection to other brain regions. However, despite its widespread use, the underlying neural mechanisms of TMS are not completely understood. Non-human primates (NHPs) offer an ideal model to study TMS mechanisms through invasive electrophysiological recordings. As such, bridging the gap between NHP experiments and human applications is imperative to ensure translational relevance. Here, we systematically compare the TMS-targeted functional networks in the prefrontal cortex in humans and NHPs. To conduct this comparison, we combine TMS electric field modeling in humans and macaques with resting-state functional magnetic resonance imaging (fMRI) data to compare the functional networks targeted via TMS across species. We identified distinct stimulation zones in macaque and human models, each exhibiting variations in the impacted networks (macaque: Frontoparietal Network, Somatomotor Network; human: Frontoparietal Network, Default Network). We identified differences in brain gyrification and functional organization across species as the underlying cause of found network differences. The TMS-network profiles we identified will allow researchers to establish consistency in network activation across species, aiding in the translational efforts to develop improved TMS functional network targeting approaches.

Comparing the efficacies of transcranial magnetic stimulation treatments using different targeting methods in major depressive disorder: protocol for a network meta-analysis

INTRODUCTION

Transcranial magnetic stimulation (TMS) over the left dorsolateral prefrontal cortex (lDLPFC) has been widely used as a treatment for major depressive disorder (MDD) in the past two decades. Different methods for localising the lDLPFC target include the '5 cm' method, the F3 method and the neuro-navigational method. However, whether TMS efficacies differ between the three targeting methods remains unclear. We present a protocol for a systematic review and network meta-analysis (NMA) to compare the efficacies of TMS treatments using these three targeting methods in MDD.

METHODS AND ANALYSIS

Relevant studies reported in English or Chinese and published up to May 2023 will be identified from searches of the following databases: PubMed, Cochrane Central Register of Controlled Trials, Embase, PsycINFO, China National Knowledge Infrastructure, Wan Fang Database, Chinese BioMedical Literature Database, and China Science and Technology Journal Database. We will include all randomised controlled trials assessing the efficacy of an active TMS treatment using any one of the three targeting methods compared with sham TMS treatment or comparing efficacies between active TMS treatments using different targeting methods. Interventions must include a minimum of 10 sessions of high-frequency TMS over the lDLPFC. The primary outcome is the reduction score of the 17-item Hamilton Depression Rating Scale, 24-item Hamilton Depression Rating Scale or Montgomery-Asberg Depression Rating Scale. The dropout rate is a secondary outcome representing the TMS treatment's acceptability. Pairwise meta-analyses and a random-effects NMA will be conducted using Stata. We will use the surface under the cumulative ranking curve to rank the different targeting methods in terms of efficacy and acceptability.

ETHICS AND DISSEMINATION

This systematic review and NMA does not require ethics approval. The results will be submitted for publication in a peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42023410273.

A novel inhibitory corticostriatal circuit that expresses mu opioid receptor-mediated synaptic plasticity

Corticostriatal circuits are generally characterized by the release of glutamate neurotransmitter from cortical terminals within the striatum. It is well known that cortical excitatory input to the dorsal striatum regulates addictive drug-related behaviors. We previously reported that anterior insular cortex (AIC) synaptic inputs to the dorsolateral striatum (DLS) control binge alcohol drinking in mice. These AIC-DLS glutamate synapses are also the sole sites of corticostriatal mu opioid receptor-mediated excitatory long-term depression (MOR-LTD) in the DLS. Recent work demonstrates that some regions of cortex send long-range, direct inhibitory inputs into the dorsal striatum. Nothing is known about the existence and regulation of AIC-DLS inhibitory synaptic transmission. Here, using a combination of patch clamp electrophysiology and optogenetics, we characterized a novel AIC-DLS corticostriatal inhibitory circuit and its regulation by MOR-mediated inhibitory LTD (MOR-iLTD). First, we found that the activation of presynaptic MORs produces MOR-iLTD in the DLS and dorsomedial striatum. Then, we showed that medium spiny neurons within the DLS receive direct inhibitory synaptic input from the cortex, specifically from the motor cortex and AIC. Using transgenic mice that express cre-recombinase within parvalbumin-expressing inhibitory neurons, we determined that this specific cortical neuron subtype sends direct GABAergic projections to the DLS. Moreover, these AIC-DLS inhibitory synaptic input subtypes express MOR-iLTD. These data suggest a novel GABAergic corticostriatal circuit that could be involved in the regulation of drug and alcohol consumption-related behaviors.

MNI2CPC: A probabilistic cortex-to-scalp mapping for non-invasive brain stimulation targeting

BACKGROUND

Synthesis of neural imaging information from many studies is valuable for identifying stable cortical targets for non-invasive brain stimulation (NIBS). Typically, these targets are specified in Montreal Neurological Institute (MNI) standard brain space. However, in practical NIBS applications, localizing MNI cortical targets often relies on the International 10-20 system or heuristic scalp approaches, which often lacks precision or applies only to specific targets.

OBJECTIVE/HYPOTHESIS

We aim to establish a probabilistic mapping from any cortical target in MNI space to continuous proportional coordinate (CPC) standard scalp space (MNI2CPC) and assess the performance of this mapping for NIBS targeting.

METHODS

The MNI2CPC mapping was calculated based on a large MRI dataset (n = 114). Its targeting error was evaluated via cross-individual validation using a leave-one-out approach, as well as through independent validation across race (n = 27) and across patient (n = 58) cohorts.

RESULTS

The cross-individual validation demonstrated targeting errors of 4.03 ± 0.69 mm on the scalp and 3.30 ± 0.59 mm in the cortex. For independent cohorts, targeting errors were 4.71 ± 0.81 mm (scalp) and 3.85 ± 0.64 mm (cortex) across race, and 4.66 ± 0.77 mm (scalp) and 3.77 ± 0.61 mm (cortex) across patient. We publish a free online tool to enable querying of the CPC coordinate for any given MNI cortical target. The resulting CPC coordinates enable rapid and accurate manual localization on the scalp in a user-friendly manner.

CONCLUSIONS

The MNI2CPC mapping developed in this study allows for manual localization of any MNI cortical target, which improves the accessibility and ease of application of NIBS in diverse settings.

Network medicine framework reveals generic herb-symptom effectiveness of traditional Chinese medicine

Understanding natural and traditional medicine can lead to world-changing drug discoveries. Despite the therapeutic effectiveness of individual herbs, traditional Chinese medicine (TCM) lacks a scientific foundation and is often considered a myth. In this study, we establish a network medicine framework and reveal the general TCM treatment principle as the topological relationship between disease symptoms and TCM herb targets on the human protein interactome. We find that proteins associated with a symptom form a network module, and the network proximity of an herb's targets to a symptom module is predictive of the herb's effectiveness in treating the symptom. These findings are validated using patient data from a hospital. We highlight the translational value of our framework by predicting herb-symptom treatments with therapeutic potential. Our network medicine framework reveals the scientific foundation of TCM and establishes a paradigm for understanding the molecular basis of natural medicine and predicting disease treatments.

Antidepressant effects of prolonged intermittent theta-burst stimulation monotherapy at the bilateral dorsomedial prefrontal cortex for medication and standard transcranial magnetic stimulation-resistant major depression: a three arm, randomized, double blind, sham-controlled pilot study

The dorsomedial prefrontal cortex (DMPFC) plays a pivotal role in depression and anxiosomatic symptom modulation. However, DMPFC stimulation using a double-cone coil has demonstrated inconsistent results for antidepressant efficacy. No study thus far has investigated the antidepressant and anti-anxiosomatic effects of prolonged intermittent theta-burst stimulation (piTBS) bilaterally over DMPFC. Furthermore, head-to-head comparisons of antidepressant effects between standard iTBS and piTBS warrant investigation. This double-blind, randomized, sham-controlled trial recruited 34 patients with highly treatment-resistant depression (TRD) unresponsive to antidepressants and standard repetitive transcranial magnetic stimulation (rTMS)/piTBS. These patients were randomly assigned to one of three monotherapy groups (standard iTBS, piTBS, or sham), with treatment administered bilaterally over the DMPFC twice per day for 3 weeks. The primary outcome was the overall changes of 17-item Hamilton Depression Rating Scale (HDRS-17) over 3-weeks intervention. The changes in Depression and Somatic Symptoms Scale (DSSS) as the secondary outcome and the anxiosomatic cluster symptoms as rated by HDRS-17 as the post-hoc outcome were measured. Multivariable generalized estimating equation analysis was performed. Although no differences in overall HDRS-17 changes between three groups were found, the antidepressant efficacy based on DSSS was higher in piTBS than in iTBS and sham at week 3 (group effect,p = 0.003, post-hoc: piTBS > iTBS, p = 0.002; piTBS > sham, p = 0.038). In post-hoc analyses, piTBS had more alleviation in anxiosomatic symptoms than iTBS (group effect, p = 0.002; post-hoc, p = 0.001). This first randomized sham-controlled study directly compared piTBS and iTBS targeting the DMPFC using a figure-of-8 coil and found piTBS may fail to demonstrate a significant antidepressant effect on overall depressive symptoms, but piTBS seems superior in alleviating anxiosomatic symptoms, even in depressed patients with high treatment resistance. This Trial registration (Registration number: NCT04037592). URL: https://clinicaltrials.gov/ct2/show/NCT04037592 .

Network-level dynamics underlying a combined rTMS and psychotherapy treatment for major depressive disorder: An exploratory network analysis

Background

Despite the growing use of repetitive transcranial magnetic stimulation (rTMS) as a treatment for depression, there is a limited understanding of the mechanisms of action and how potential treatment-related brain changes help to characterize treatment response. To address this gap in understanding we investigated the effects of an approach combining rTMS with simultaneous psychotherapy on global functional connectivity.

Method

We compared task-related functional connectomes based on an idiographic goal priming task tied to emotional regulation acquired before and after simultaneous rTMS/psychotherapy treatment for patients with major depressive disorders and compared these changes to normative connectivity patterns from a set of healthy volunteers (HV) performing the same task.

Results

At baseline, compared to HVs, patients demonstrated hyperconnectivity of the DMN, cerebellum and limbic system, and hypoconnectivity of the fronto-parietal dorsal-attention network and visual cortex. Simultaneous rTMS/psychotherapy helped to normalize these differences, which were reduced after treatment. This finding suggests that the rTMS/therapy treatment regularizes connectivity patterns in both hyperactive and hypoactive brain networks.

Conclusions

These results help to link treatment to a comprehensive model of the neurocircuitry underlying depression and pave the way for future studies using network-guided principles to significantly improve rTMS efficacy for depression.

An Evaluation of the Effectiveness of Repetitive Transcranial Magnetic Stimulation (rTMS) for the Management of Treatment-Resistant Depression with Somatic Attributes: A Hospital-Based Study in Oman

Depressive illnesses in non-Western societies are often masked by somatic attributes that are sometimes impervious to pharmacological agents. This study explores the effectiveness of repetitive transcranial magnetic stimulation (rTMS) for people experiencing treatment-resistant depression (TRD) accompanied by physical symptoms. Data were obtained from a prospective study conducted among patients with TRD and some somatic manifestations who underwent 20 sessions of rTMS intervention from January to June 2020. The Hamilton Rating Scale for Depression (HAMD) was used for clinical evaluation. Data were analysed using descriptive and inferential techniques (multiple logistic regression) in SPSS. Among the 49 participants (mean age: 42.5 ± 13.3), there was a significant reduction in posttreatment HAMD scores compared to baseline (t = 10.819, p < 0.0001, and 95% CI = 8.574-12.488), indicating a clinical response. Approximately 37% of the patients responded to treatment, with higher response rates among men and those who remained in urban areas, had a history of alcohol use, and were subjected to the standard 10 HZ protocol. After adjusting for all extraneous variables, the rTMS protocol emerged as the only significant predictor of response to the rTMS intervention. To our knowledge, this is the first study to examine the effectiveness of rTMS in the treatment of somatic depression.

A randomized trial comparing beam F3 and 5.5 cm targeting in rTMS treatment of depression demonstrates similar effectiveness

BACKGROUND

The Beam F3 and 5.5 cm methods are the two most common targeting strategies for localizing the left dorsolateral prefrontal cortex (DLPFC) treatment site in repetitive transcranial magnetic stimulation (rTMS) protocols. This prospective, randomized, double-blind comparative effectiveness trial assesses the clinical outcomes for these two methods in a naturalistic sample of patients with major depressive disorder (MDD) undergoing clinical rTMS treatment.

METHODS

105 adult patients with MDD (mean age = 43.2; range = 18-73; 66% female) were randomized to receive rTMS to the Beam F3 (n = 58) or 5.5 cm (n = 47) target. Between group differences from pre-to post-treatment were evaluated with the Patient Health Questionnaire-9 (PHQ-9) [primary outcome measure], Generalized Anxiety Disorder-7 (GAD-7), and clinician-administered Montgomery-Åsberg Depression Scale (MADRS). Primary treatment endpoint was completion of daily treatment series.

RESULTS

Per-protocol analyses showed no statistically significant differences on any measure between the 5.5 cm and F3 groups (all p ≥ 0.50), including percent improvement (PHQ-9: 39% vs. 39%; GAD-7: 34% vs. 27%; MADRS: 40% vs. 38%), response rate (PHQ-9: 37% vs. 43%; GAD-7: 27% vs. 30%; MADRS: 43% vs. 43%), and remission rate (PHQ-9: 22% vs. 21%; MADRS: 20% vs. 19%). Post hoc analysis of anxiety symptom change while controlling for depression severity suggested more favorable anxiolytic effects with 5.5 cm targeting (p = 0.03).

CONCLUSIONS

Similar antidepressant effects were observed with DLFPC rTMS using either the Beam F3 or 5.5 cm targeting method, supporting clinical equipoise in MDD patients with head circumference ≤ 60 cm. Comparison to MRI-based targeting and differential effects on anxiety symptoms require further investigation.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03378570.

Connectome-based prediction of eating disorder-associated symptomatology

BACKGROUND

Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM).

METHODS

CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants.

RESULTS

The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect.

CONCLUSIONS

These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.

Functional connectomics in depression: insights into therapies

Depression is a common mental disorder characterized by heterogeneous cognitive and behavioral symptoms. The emerging research paradigm of functional connectomics has provided a quantitative theoretical framework and analytic tools for parsing variations in the organization and function of brain networks in depression. In this review, we first discuss recent progress in depression-associated functional connectome variations. We then discuss treatment-specific brain network outcomes in depression and propose a hypothetical model highlighting the advantages and uniqueness of each treatment in relation to the modulation of specific brain network connectivity and symptoms of depression. Finally, we look to the future promise of combining multiple treatment types in clinical practice, using multisite datasets and multimodal neuroimaging approaches, and identifying biological depression subtypes.

Interventional Mental Health: A Transdisciplinary Approach to Novel Psychiatric Care Delivery

Mental health disorders are among the most common health conditions in the United States. Traditional clinical treatments rely on psychiatric counseling and, in many cases, prescription medications. We propose an innovative model, Interventional Mental Health, which employs a combination of modalities through a multifaceted approach to treat conditions that have exhibited limited responsiveness to traditional methods and individuals afflicted with multiple comorbidities simultaneously. We hypothesize that creating a unique treatment algorithm combining current therapeutic modalities such as Stellate Ganglion Blocks (SGB), Transcranial Magnetic Stimulation (TMS) therapy, and ketamine therapy, within a consolidated timeframe, will yield synergistic outcomes among patients presenting with comorbid post-traumatic stress disorder (PTSD), depression, and/or anxiety.

Connectomic insight into unique stroke patient recovery after rTMS treatment

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

Precision functional MRI mapping reveals distinct connectivity patterns for depression associated with traumatic brain injury

Depression associated with traumatic brain injury (TBI) is believed to be clinically distinct from primary major depressive disorder (MDD) and may be less responsive to conventional treatments. Brain connectivity differences between the dorsal attention network (DAN), default mode network (DMN), and subgenual cingulate have been implicated in TBI and MDD. To characterize these distinctions, we applied precision functional mapping of brain network connectivity to resting-state functional magnetic resonance imaging data from five published patient cohorts, four discovery cohorts (n = 93), and one replication cohort (n = 180). We identified a distinct brain connectivity profile in TBI-associated depression that was independent of TBI, MDD, posttraumatic stress disorder (PTSD), depression severity, and cohort. TBI-associated depression was independently associated with decreased DAN-subgenual cingulate connectivity, increased DAN-DMN connectivity, and the combined effect of both. This effect was stronger when using precision functional mapping relative to group-level network maps. Our results support the possibility of a physiologically distinct "TBI affective syndrome," which may benefit from individualized neuromodulation approaches to target its distinct neural circuitry.