Personalized Transcranial Magnetic Stimulation in Psychiatry

Reproducible routes: reliably navigating the connectome to enrich personalized brain stimulation strategies

Non-invasive brain stimulation (NIBS) technologies, such as repetitive transcranial magnetic stimulation (rTMS), offer significant therapeutic potential for a growing number of neuropsychiatric conditions. Concurrent with the expansion of this field is the swift evolution of rTMS methodologies, including approaches to optimize stimulation site planning. Traditional targeting methods, foundational to early successes in the field and still widely employed today, include using scalp-based heuristics or integrating structural MRI co-registration to align the transcranial magnetic stimulation (TMS) coil with anatomical landmarks. Recent evidence, however, supports refining and personalizing stimulation sites based on the target's structural and/or functional connectivity profile. These connectomic approaches harness the network-wide neuromodulatory effects of rTMS to reach deeper brain structures while also enabling a greater degree of personalization by accounting for heterogenous network topology. In this study, we acquired baseline multimodal magnetic resonance (MRI) at two time points to evaluate the reliability and reproducibility of distinct connectome-based strategies for stimulation site planning. Specifically, we compared the intra-individual difference between the optimal stimulation sites generated at each time point for (1) functional connectivity (FC) guided targets derived from resting-state functional MRI and (2) structural connectivity (SC) guided targets derived from diffusion tensor imaging. Our findings suggest superior reproducibility of SC-guided targets. We emphasize the necessity for further research to validate these findings across diverse patient populations, thereby advancing the personalization of rTMS treatments.

Effects of repetitive transcranial magnetic stimulation on inhibitory control in first-episode schizophrenia: behavioral and neural mechanisms

Background

Inhibitory control deficits are a core feature of cognitive impairment in schizophrenia, associated with abnormal activation of key brain networks. Repetitive transcranial magnetic stimulation (rTMS) targeting the dorsolateral prefrontal cortex (DLPFC) may help improve inhibitory control, but its specific effects in schizophrenia remain uncertain.

Methods

This study involved 150 participants divided into Real-rTMS, Sham-rTMS, and healthy control groups. Inhibitory control was assessed using the dual-choice oddball task, and task-based functional magnetic resonance imaging (fMRI) was employed to examine neural activity. The Real-rTMS group received active stimulation over the DLPFC, and the Sham group received placebo stimulation.

Results

The Real-rTMS group exhibited significant improvements in both reaction times and accuracy compared to the Sham group, indicating enhanced inhibitory control. fMRI data showed that brain activity in regions such as the cerebellum, insula, thalamus, and prefrontal cortex was normalized in the Real-rTMS group, with activation patterns closely resembling those observed in healthy controls. Additionally, task-based fMRI revealed a restoration and further enhancement of negative activation in regions like the middle frontal gyrus and superior temporal gyrus, which helped reduce cognitive interference from irrelevant stimuli.

Conclusion

rTMS targeting the DLPFC improves inhibitory control in schizophrenia by modulating both positive and negative brain activation patterns. These findings highlight the dual mechanism through which rTMS enhances cognitive control, offering a promising intervention for cognitive deficits in schizophrenia. Future research should explore the long-term effects of this modulation on broader cognitive functions.

Monitoring Changes in TMS-Evoked EEG and EMG Activity During 1 Hz rTMS of the Healthy Motor Cortex

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation technique capable of inducing neuroplasticity as measured by changes in peripheral muscle electromyography (EMG) or electroencephalography (EEG) from pre-to-post stimulation. However, temporal courses of neuromodulation during ongoing rTMS are unclear. Monitoring cortical dynamics via TMS-evoked responses using EMG (motor-evoked potentials; MEPs) and EEG (transcranial-evoked potentials; TEPs) during rTMS might provide further essential insights into its mode of action - temporal course of potential modulations. The objective of this study was to first evaluate the validity of online rTMS-EEG and rTMS-EMG analyses, and second to scrutinize the temporal changes of TEPs and MEPs during rTMS. As rTMS is subject to high inter-individual effect variability, we aimed for single-subject analyses of EEG changes during rTMS. Ten healthy human participants were stimulated with 1,000 pulses of 1 Hz rTMS over the motor cortex, while EEG and EMG were recorded continuously. Validity of MEPs and TEPs measured during rTMS was assessed in sensor and source space. Electrophysiological changes during rTMS were evaluated with model fitting approaches on a group- and single-subject level. TEPs and MEPs appearance during rTMS was consistent with past findings of single pulse experiments. Heterogeneous temporal progressions, fluctuations or saturation effects of brain activity were observed during rTMS depending on the TEP component. Overall, global brain activity increased over the course of stimulation. Single-subject analysis revealed inter-individual temporal courses of global brain activity. The present findings are in favor of dose-response considerations and attempts in personalization of rTMS protocols.

Efficacy of functional magnetic resonance imaging-guided personalized repetitive transcranial magnetic stimulation (fMRI-rTMS) in depressive patients with emotional blunting: study protocol for a randomized controlled trial

BACKGROUND

Emotional blunting is a symptom that has always been present in depressed patients. Repetitive transcranial magnetic stimulation (rTMS) is a safe and effective supplementary therapy for treating depression. However, the effectiveness and brain imaging processes of functional magnetic resonance imaging-guided personalized rTMS (fMRI-rTMS) in the treatment of depression with emotional blunting have not been observed in randomized controlled trials.

METHODS

This study is a randomized, controlled, double-blind, and single-center clinical trial in which 80 eligible depressed patients with emotional blunting will be randomly assigned to two groups: a functional magnetic resonance imaging-guided personalized rTMS (fMRI-rTMS) group and a control group. Individuals in the fMRI-rTMS group (n = 40) will receive high-frequency rTMS (10 Hz, 120% MT). The main target of stimulation will be the area most relevant to the functional connectivity of the right medial prefrontal cortex (mPFC) and amygdala. The control group (n = 40) will receive sham stimulation, with a coil flipped to 90 degrees relative to the vertical scalp. All patients will receive 15 consecutive days of treatment, with each session lasting half an hour per day, followed by 8 weeks of follow-up. The primary outcome is the comparison of Oxford Depression Questionnaire (ODQ) scores between these two groups at different time points. The secondary outcomes include evaluating other clinical scales and assessing the differences in brain imaging changes between the two groups before and after treatment.

DISCUSSION

This trial aims to examine the effects of functional magnetic resonance imaging-guided personalized rTMS (fMRI-rTMS) intervention on depressed patients experiencing emotional blunting and to elucidate the potential mechanism behind it. The results will provide new evidence for using fMRI-rTMS in treating depression with emotional blunting in the future.

TRIAL REGISTRATION

ClinicalTrials.gov INCT05555940. Registered on 13 September 2022 at http://clinicaltrials.gov .

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.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

A formalism for sequential estimation of neural membrane time constant and input--output curve towards selective and closed-loop transcranial magnetic stimulation

OBJECTIVE

To obtain a formalism for real-time concurrent sequential estimation of neural membrane time constant and input--output (IO) curve with transcranial magnetic stimulation (TMS).

APPROACH

First, the neural membrane response and depolarization factor, which leads to motor evoked potentials (MEPs) with TMS are analytically computed and discussed. Then, an integrated model is developed which combines the neural membrane time constant and input--output curve. Identifiability of the proposed integrated model is discussed. A condition is derived, which assures estimation of the proposed integrated model. Finally, sequential parameter estimation (SPE) of the neural membrane time constant and IO curve is described through closed-loop optimal sampling and open-loop uniform sampling TMS. Without loss of generality, this paper focuses on a specific case of commercialized TMS pulse shapes. The proposed formalism and SPE method are directly applicable to other pulse shapes.

MAIN RESULTS

The results confirm satisfactory estimation of the membrane time constant and IO curve parameters. By defining a stopping rule based on five times consecutive convergence of the estimation parameters with a tolerances of 0.01, the membrane time constant and IO curve parameters are estimated with 82 TMS pulses with absolute relative estimation errors (AREs) of less than 4% with the optimal sampling SPE method. At this point, the uniform sampling SPE method leads to AREs up to 16%. The uniform sampling method does not satisfy the stopping rule due to the large estimation variations.

SIGNIFICANCE

This paper provides a tool for real-time closed-loop SPE of the neural time constant and IO curve, which can contribute novel insights in TMS studies. SPE of the membrane time constant enables selective stimulation, which can be used for advanced brain research, precision medicine and personalized medicine.

Where do we stand now regarding treatment of psychiatric and neurodegenerative disorders? Considerations in using magnetoelectric nanoparticles as an innovative approach

Almost 1000 million people have recently been diagnosed with a mental health or substance disorder (Ritchie & Roser, 2018). Psychiatric disorders, and their treatment, represent a big burden to the society worldwide, causing about 8 million deaths per year (Walker et al., 2015). Daily progress in science enables continuous advances in methods to treat patients; however, the brain remains to be the most unknown and complex organ of the body. There is a growing demand for innovative approaches to treat psychiatric as well as neurodegenerative disorders, disorders with unknown curability, and treatments mostly designed to slow disease progression. Based on that need and the peculiarity of the central nervous system, in the present review, we highlight the handicaps of the existing approaches as well as discuss the potential of the recently introduced magnetoelectric nanoparticles (MENPs) to become a game-changing tool in future applications for the treatment of brain alterations. Unlike other stimulation approaches, MENPs have the potential to enable a wirelessly controlled stimulation at a single-neuron level without requiring genetic modification of the neural tissue and no toxicity has yet been reported. Their potential as a new tool for targeting the brain is discussed. This article is categorized under: Therapeutic Approaches and Drug Discovery > Nanomedicine for Cardiovascular Disease Therapeutic Approaches and Drug Discovery > Neurological Disease.

[Brain Stimulation for the Treatment of Dementia]

Due to the increasing number of cases of Alzheimer's disease and the relatively moderate success with the available symptomatic and causal pharmacological therapies, there is a considerable need to explore non-pharmacological treatment options. In the field of non-invasive brain stimulation (NIBS), various methods have been investigated, particularly transcranial magnetic stimulation and transcranial electrical stimulation. In addition, deep brain stimulation (DBS) is currently being researched as an innovative method for targeted neuromodulation. Both non-invasive and invasive approaches aim to modulate neuronal activity and improve cognitive-mnestic functions. Secondary mechanisms such as long-term potentiation in NIBS or neurogenesis in DBS could also achieve long-term positive effects. Preclinical and clinical studies have already shown promising results in patients in early stages of Alzheimer's disease. However, inconsistent study and stimulation protocols and small sample sizes make it difficult to assess efficacy. Further research is warranted to enable the use of non-invasive or invasive neuromodulatory approaches in clinical practice in the near future.

Neural Substrates of Poststroke Depression: Current Opinions and Methodology Trends

Poststroke depression (PSD), affecting about one-third of stroke survivors, exerts significant impact on patients’ functional outcome and mortality. Great efforts have been made since the 1970s to unravel the neuroanatomical substrate and the brain-behavior mechanism of PSD. Thanks to advances in neuroimaging and computational neuroscience in the past two decades, new techniques for uncovering the neural basis of symptoms or behavioral deficits caused by focal brain damage have been emerging. From the time of lesion analysis to the era of brain networks, our knowledge and understanding of the neural substrates for PSD are increasing. Pooled evidence from traditional lesion analysis, univariate or multivariate lesion-symptom mapping, regional structural and functional analyses, direct or indirect connectome analysis, and neuromodulation clinical trials for PSD, to some extent, echoes the frontal-limbic theory of depression. The neural substrates of PSD may be used for risk stratification and personalized therapeutic target identification in the future. In this review, we provide an update on the recent advances about the neural basis of PSD with the clinical implications and trends of methodology as the main features of interest.

Identification of the sociodemographic and clinical characteristics of the patients who have undergone transcranial magnetic stimulation in a psychiatry clinic: A retrospective descriptive design

PURPOSE

The aim of this study, which is the first in this field in Turkey, is to determine the sociodemographic and clinical characteristics of patients who have undergone transcranial magnetic stimulation (TMS) in a psychiatry clinic.

DESIGN AND METHODS

This study has a retrospective descriptive design. Data of 513 psychiatric patients who have undergone TMS between 2015 and 2018 in a university hospital were reviewed.

FINDINGS

Significant differences were found between psychiatric diagnoses of the patients, based on their sex, marital status, and the number of courses of treatment with TMS (p < 0.05).

PRACTICAL IMPLICATIONS

It was suggested that nurses who would practice this procedure were required to be educated for TMS and nursing care to provide well and effective care.

Revisiting Hemispheric Asymmetry in Mood Regulation: Implications for rTMS for Major Depressive Disorder

Hemispheric differences in emotional processing have been observed for over half a century, leading to multiple theories classifying differing roles for the right and left hemisphere in emotional processing. Conventional acceptance of these theories has had lasting clinical implications for the treatment of mood disorders. The theory that the left hemisphere is broadly associated with positively valenced emotions, while the right hemisphere is broadly associated with negatively valenced emotions, drove the initial application of repetitive transcranial magnetic stimulation (rTMS) for the treatment of major depressive disorder (MDD). Subsequent rTMS research has led to improved response rates while adhering to the same initial paradigm of administering excitatory rTMS to the left prefrontal cortex (PFC) and inhibitory rTMS to the right PFC. However, accumulating evidence points to greater similarities in emotional regulation between the hemispheres than previously theorized, with potential implications for how rTMS for MDD may be delivered and optimized in the near future. This review will catalog the range of measurement modalities that have been used to explore and describe hemispheric differences, and highlight evidence that updates and advances knowledge of TMS targeting and parameter selection. Future directions for research are proposed that may advance precision medicine and improve efficacy of TMS for MDD.

Focal neural perturbations reshape low-dimensional trajectories of brain activity supporting cognitive performance

The emergence of distributed patterns of neural activity supporting brain functions and behavior can be understood by study of the brain's low-dimensional topology. Functional neuroimaging demonstrates that brain activity linked to adaptive behavior is constrained to low-dimensional manifolds. In human participants, we tested whether these low-dimensional constraints preserve working memory performance following local neuronal perturbations. We combined multi-session functional magnetic resonance imaging, non-invasive transcranial magnetic stimulation (TMS), and methods translated from the fields of complex systems and computational biology to assess the functional link between changes in local neural activity and the reshaping of task-related low dimensional trajectories of brain activity. We show that specific reconfigurations of low-dimensional trajectories of brain activity sustain effective working memory performance following TMS manipulation of local activity on, but not off, the space traversed by these trajectories. We highlight an association between the multi-scale changes in brain activity underpinning cognitive function.

Distributed Subnetworks of Depression Defined by Direct Intracranial Neurophysiology

Major depressive disorder is a common and disabling disorder with high rates of treatment resistance. Evidence suggests it is characterized by distributed network dysfunction that may be variable across patients, challenging the identification of quantitative biological substrates. We carried out this study to determine whether application of a novel computational approach to a large sample of high spatiotemporal resolution direct neural recordings in humans could unlock the functional organization and coordinated activity patterns of depression networks. This group level analysis of depression networks from heterogenous intracranial recordings was possible due to application of a correlational model-based method for inferring whole-brain neural activity. We then applied a network framework to discover brain dynamics across this model that could classify depression. We found a highly distributed pattern of neural activity and connectivity across cortical and subcortical structures that was present in the majority of depressed subjects. Furthermore, we found that this depression signature consisted of two subnetworks across individuals. The first was characterized by left temporal lobe hypoconnectivity and pathological beta activity. The second was characterized by a hypoactive, but hyperconnected left frontal cortex. These findings have applications toward personalization of therapy.

Wearable wireless power systems for 'ME-BIT' magnetoelectric-powered bio implants

Objective.Compared to biomedical devices with implanted batteries, wirelessly powered technologies can be longer-lasting, less invasive, safer, and can be miniaturized to access difficult-to-reach areas of the body. Magnetic fields are an attractive wireless power transfer modality for such bioelectronic applications because they suffer negligible absorption and reflection in biological tissues. However, current solutions using magnetic fields for mm sized implants either operate at high frequencies (>500 kHz) or require high magnetic field strengths (>10  mT), which restricts the amount of power that can be transferred safely through tissue and limits the development of wearable power transmitter systems. Magnetoelectric (ME) materials have recently been shown to provide a wireless power solution for mm-sized neural stimulators. These ME transducers convert low magnitude (<1 mT) and low-frequency (∼300 kHz) magnetic fields into electric fields that can power custom integrated circuits or stimulate nearby tissue.Approach.Here we demonstrate a battery-powered wearable magnetic field generator that can power a miniaturized MagnetoElectric-powered Bio ImplanT 'ME-BIT' that functions as a neural stimulator. The wearable transmitter weighs less than 0.5  lbs and has an approximate battery life of 37 h.Main results.We demonstrate the ability to power a millimeter-sized prototype 'ME-BIT' at a distance of 4 cm with enough energy to electrically stimulate a rat sciatic nerve. We also find that the system performs well under translational misalignment and identify safe operating ranges according to the specific absorption rate limits set by the IEEE Std 95.1-2019.Significance.These results validate the feasibility of a wearable system that can power miniaturized ME implants that can be used for different neuromodulation applications.

Functional Imaging to Guide Network-Based TMS Treatments: Toward a Tailored Medicine Approach in Alzheimer's Disease

A growing number of studies is using fMRI-based connectivity to guide transcranial magnetic stimulation (TMS) target identification in both normal and clinical populations. TMS has gained increasing attention as a potential therapeutic strategy also in Alzheimer's disease (AD), but an endorsed target localization strategy in this population is still lacking. In this proof of concept study, we prove the feasibility of a tailored TMS targeting approach for AD, which stems from a network-based perspective. Based on functional imaging, the procedure allows to extract individual optimal targets meanwhile accounting for functional variability. Single-subject resting-state fMRI was used to extract individual target coordinates of two networks primarily affected in AD, the default mode and the fronto-parietal network. The localization of these targets was compared to that of traditional group-level approaches and tested against varying degrees of TMS focality. The distance between individual fMRI-derived coordinates and traditionally defined targets was significant for a supposed TMS focality of 12 mm and in some cases up to 20 mm. Comparison with anatomical labels confirmed a lack of 1:1 correspondence between anatomical and functional targets. The proposed network-based fMRI-guided TMS approach, while accounting for inter-individual functional variability, allows to target core AD networks, and might thus represent a step toward tailored TMS interventions for AD.

Individual deviations from normative models of brain structure in a large cross-sectional schizophrenia cohort

The heterogeneity of schizophrenia has defied efforts to derive reproducible and definitive anatomical maps of structural brain changes associated with the disorder. We aimed to map deviations from normative ranges of brain structure for individual patients and evaluate whether the loci of individual deviations recapitulated group-average brain maps of schizophrenia pathology. For each of 48 white matter tracts and 68 cortical regions, normative percentiles of variation in fractional anisotropy (FA) and cortical thickness (CT) were established using diffusion-weighted and structural MRI from healthy adults (n = 195). Individuals with schizophrenia (n = 322) were classified as either within the normative range for healthy individuals of the same age and sex (5-95% percentiles), infra-normal (<5% percentile) or supra-normal (>95% percentile). Repeating this classification for each tract and region yielded a deviation map for each individual. Compared to the healthy comparison group, the schizophrenia group showed widespread reductions in FA and CT, involving virtually all white matter tracts and cortical regions. Paradoxically, however, no more than 15-20% of patients deviated from the normative range for any single tract or region. Furthermore, 79% of patients showed infra-normal deviations for at least one locus (healthy individuals: 59 ± 2%, p < 0.001). Thus, while infra-normal deviations were common among patients, their anatomical loci were highly inconsistent between individuals. Higher polygenic risk for schizophrenia associated with a greater number of regions with infra-normal deviations in CT (r = -0.17, p = 0.006). We conclude that anatomical loci of schizophrenia-related changes are highly heterogeneous across individuals to the extent that group-consensus pathological maps are not representative of most individual patients. Normative modeling can aid in parsing schizophrenia heterogeneity and guiding personalized interventions.

Activity flow underlying abnormalities in brain activations and cognition in schizophrenia

Cognitive dysfunction is a core feature of many brain disorders, including schizophrenia (SZ), and has been linked to aberrant brain activations. However, it is unclear how these activation abnormalities emerge. We propose that aberrant flow of brain activity across functional connectivity (FC) pathways leads to altered activations that produce cognitive dysfunction in SZ. We tested this hypothesis using activity flow mapping, an approach that models the movement of task-related activity between brain regions as a function of FC. Using functional magnetic resonance imaging data from SZ individuals and healthy controls during a working memory task, we found that activity flow models accurately predict aberrant cognitive activations across multiple brain networks. Within the same framework, we simulated a connectivity-based clinical intervention, predicting specific treatments that normalized brain activations and behavior in patients. Our results suggest that dysfunctional task-evoked activity flow is a large-scale network mechanism contributing to cognitive dysfunction in SZ.

Heading for Personalized rTMS in Tinnitus: Reliability of Individualized Stimulation Protocols in Behavioral and Electrophysiological Responses

Background: Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive brain stimulation tool potentially modulating pathological brain activity. Its clinical effectiveness is hampered by varying results and characterized by inter-individual variability in treatment responses. RTMS individualization might constitute a useful strategy to overcome this variability. A precondition for this approach would be that repeatedly applied protocols result in reliable effects. The condition tinnitus provides the advantage of immediate behavioral consequences (tinnitus loudness changes) after interventions and thus offers an excellent model to exemplify TMS personalization. Objective: The aim was to investigate the test–retest reliability of short rTMS stimulations in modifying tinnitus loudness and oscillatory brain activity as well as to examine the feasibility of rTMS individualization in tinnitus. Methods: Three short verum (1, 10, 20 Hz; 200 pulses) and one sham (0.1 Hz; 20 pulses) rTMS protocol were administered on two different days in 22 tinnitus patients. Before and after each protocol, oscillatory brain activity was recorded with electroencephalography (EEG), together with behavioral tinnitus loudness ratings. RTMS individualization was executed on the basis of behavioral and electrophysiological responses. Stimulation responders were identified via consistent sham-superior increases in tinnitus loudness (behavioral responders) and alpha power increases or gamma power decreases (alpha responders/gamma responders) in accordance with the prevalent neurophysiological models for tinnitus. Results: It was feasible to identify individualized rTMS protocols featuring reliable tinnitus loudness changes (55% behavioral responder), alpha increases (91% alpha responder) and gamma decreases (100% gamma responder), respectively. Alpha responses primary occurred over parieto-occipital areas, whereas gamma responses mainly appeared over frontal regions. On the contrary, test–retest correlation analyses per protocol at a group level were not significant neither for behavioral nor for electrophysiological effects. No associations between behavioral and EEG responses were found. Conclusion: RTMS individualization via behavioral and electrophysiological data in tinnitus can be considered as a feasible approach to overcome low reliability at the group level. The present results open the discussion favoring personalization utilizing neurophysiological markers rather than behavioral responses. These insights are not only useful for the rTMS treatment of tinnitus but also for neuromodulation interventions in other pathologies, as our results suggest that the individualization of stimulation protocols is feasible despite absent group-level reliability.

Pre-supplementary motor network connectivity and clinical outcome of magnetic stimulation in obsessive-compulsive disorder

A large proportion of patients with obsessive–compulsive disorder (OCD) respond unsatisfactorily to pharmacological and psychological treatments. An alternative novel treatment for these patients is repetitive transcranial magnetic stimulation (rTMS). This study aimed to investigate the underlying neural mechanism of rTMS treatment in OCD patients. A total of 37 patients with OCD were randomized to receive real or sham 1‐Hz rTMS (14 days, 30 min/day) over the right pre‐supplementary motor area (preSMA). Resting‐state functional magnetic resonance imaging data were collected before and after rTMS treatment. The individualized target was defined by a personalized functional connectivity map of the subthalamic nucleus. After treatment, patients in the real group showed a better improvement in the Yale–Brown Obsessive Compulsive Scale than the sham group (F_1,35 = 6.0, p = .019). To show the neural mechanism involved, we identified an “ideal target connectivity” before treatment. Leave‐one‐out cross‐validation indicated that this connectivity pattern can significantly predict patients' symptom improvements (r = .60, p = .009). After real treatment, the average connectivity strength of the target network significantly decreased in the real but not in the sham group. This network‐level change was cross‐validated in three independent datasets. Altogether, these findings suggest that personalized magnetic stimulation on preSMA may alleviate obsessive–compulsive symptoms by decreasing the connectivity strength of the target network.

Personalized connectivity-guided DLPFC-TMS for depression: Advancing computational feasibility, precision and reproducibility

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) is an established treatment for refractory depression, however, therapeutic outcomes vary. Mounting evidence suggests that clinical response relates to functional connectivity with the subgenual cingulate cortex (SGC) at the precise DLPFC stimulation site. Critically, SGC-related network architecture shows considerable interindividual variation across the spatial extent of the DLPFC, indicating that connectivity-based target personalization could potentially be necessary to improve treatment outcomes. However, to date accurate personalization has not appeared feasible, with recent work indicating that the intraindividual reproducibility of optimal targets is limited to 3.5 cm. Here we developed reliable and accurate methodologies to compute individualized connectivity-guided stimulation targets. In resting-state functional MRI scans acquired across 1,000 healthy adults, we demonstrate that, using this approach, personalized targets can be reliably and robustly pinpointed, with a median accuracy of ~2 mm between scans repeated across separate days. These targets remained highly stable, even after 1 year, with a median intraindividual distance between coordinates of only 2.7 mm. Interindividual spatial variation in personalized targets exceeded intraindividual variation by a factor of up to 6.85, suggesting that personalized targets did not trivially converge to a group-average site. Moreover, personalized targets were heritable, suggesting that connectivity-guided rTMS personalization is stable over time and under genetic control. This computational framework provides capacity for personalized connectivity-guided TMS targets to be robustly computed with high precision and has the flexibly to advance research in other basic research and clinical applications.

Considerations for Pairing Cognitive Behavioral Therapies and Non-invasive Brain Stimulation: Ignore at Your Own Risk

Multimodal approaches combining cognitive behavioral therapies (CBT) with non-invasive brain stimulation (NIBS) hold promise for improving the treatment of neuropsychiatric disorders. As this is a relatively new approach, it is a critical time to identify guiding principles and methodological considerations to enhance research rigor. In the current paper, we argue for a principled approach to CBT and NIBS pairings based on synergistic activation of neural circuits and identify key considerations about CBT that may influence pairing with NIBS. Careful consideration of brain-state interactions and CBT-related nuances will increase the potential for these combinations to be positively synergistic.

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Rhythmic activity in the brain fluctuates with behaviour and cognitive state, through a combination of coexisting and interacting frequencies. At large spatial scales such as those studied in human M/EEG, measured oscillatory dynamics are believed to arise primarily from a combination of cortical (intracolumnar) and corticothalamic rhythmogenic mechanisms. Whilst considerable progress has been made in characterizing these two types of neural circuit separately, relatively little work has been done that attempts to unify them into a single consistent picture. This is the aim of the present paper. We present and examine a whole-brain, connectome-based neural mass model with detailed long-range cortico-cortical connectivity and strong, recurrent corticothalamic circuitry. This system reproduces a variety of known features of human M/EEG recordings, including spectral peaks at canonical frequencies, and functional connectivity structure that is shaped by the underlying anatomical connectivity. Importantly, our model is able to capture state- (e.g., idling/active) dependent fluctuations in oscillatory activity and the coexistence of multiple oscillatory phenomena, as well as frequency-specific modulation of functional connectivity. We find that increasing the level of sensory drive to the thalamus triggers a suppression of the dominant low frequency rhythms generated by corticothalamic loops, and subsequent disinhibition of higher frequency endogenous rhythmic behaviour of intracolumnar microcircuits. These combine to yield simultaneous decreases in lower frequency and increases in higher frequency components of the M/EEG power spectrum during states of high sensory or cognitive drive. Building on this, we also explored the effect of pulsatile brain stimulation on ongoing oscillatory activity, and evaluated the impact of coexistent frequencies and state-dependent fluctuations on the response of cortical networks. Our results provide new insight into the role played by cortical and corticothalamic circuits in shaping intrinsic brain rhythms, and suggest new directions for brain stimulation therapies aimed at state-and frequency-specific control of oscillatory brain activity.

Precision Inhibitory Stimulation of Individual-Specific Cortical Hubs Disrupts Information Processing in Humans

Noninvasive brain stimulation (NIBS) is a promising treatment for psychiatric and neurologic conditions, but outcomes are variable across treated individuals. In principle, precise targeting of individual-specific features of functional brain networks could improve the efficacy of NIBS interventions. Network theory predicts that the role of a node in a network can be inferred from its connections; as such, we hypothesized that targeting individual-specific "hub" brain areas with NIBS should impact cognition more than nonhub brain areas. Here, we first demonstrate that the spatial positioning of hubs is variable across individuals but reproducible within individuals upon repeated imaging. We then tested our hypothesis in healthy individuals using a prospective, within-subject, double-blind design. Inhibition of a hub with continuous theta burst stimulation disrupted information processing during working-memory more than inhibition of a nonhub area, despite targets being separated by only a few centimeters on the right middle frontal gyrus of each subject. Based upon these findings, we conclude that individual-specific brain network features are functionally relevant and could leveraged as stimulation sites in future NIBS interventions.

Topographic organization of the human subcortex unveiled with functional connectivity gradients

Brain atlases are fundamental to understanding the topographic organization of the human brain, yet many contemporary human atlases cover only the cerebral cortex, leaving the subcortex a terra incognita. We use functional MRI (fMRI) to map the complex topographic organization of the human subcortex, revealing large-scale connectivity gradients and new areal boundaries. We unveil four scales of subcortical organization that recapitulate well-known anatomical nuclei at the coarsest scale and delineate 27 new bilateral regions at the finest. Ultrahigh field strength fMRI corroborates and extends this organizational structure, enabling the delineation of finer subdivisions of the hippocampus and the amygdala, while task-evoked fMRI reveals a subtle subcortical reorganization in response to changing cognitive demands. A new subcortical atlas is delineated, personalized to represent individual differences and used to uncover reproducible brain-behavior relationships. Linking cortical networks to subcortical regions recapitulates a task-positive to task-negative axis. This new atlas enables holistic connectome mapping and characterization of cortico-subcortical connectivity.

Use of EEG for Predicting Treatment Response to Transcranial Magnetic Stimulation in Obsessive Compulsive Disorder

Aim. In this study we assessed the predictive power of quantitative EEG (qEEG) for the treatment response to right frontal transcranial magnetic stimulation (TMS) in obsessive compulsive disorder (OCD) using a machine learning approach. Method. The study included 50 OCD patients (35 responsive to TMS, 15 nonresponsive) who were treated with right frontal low frequency stimulation and identified retrospectively from Uskudar Unversity, NPIstanbul Brain Hospital outpatient clinic. All patients were diagnosed with OCD according to the DSM-IV-TR and DSM-5 criteria. We first extracted pretreatment band powers for patients. To explore the prediction accuracy of pretreatment EEG, we employed machine learning methods using an artificial neural network model. Results. Among 4 EEG bands, theta power successfully discriminated responsive from nonresponsive patients. Responsive patients had more theta powers for all electrodes as compared to nonresponsive patients. Discussion. qEEG could be helpful before deciding about treatment strategy in OCD. The limitations of our study are moderate sample size and limited number of nonresponsive patients and that treatment response was defined by clinicians and not by using a formal symptom measurement scale. Future studies with larger samples and prospective design would show the role of qEEG in predicting TMS response better.

Precision non-implantable neuromodulation therapies: a perspective for the depressed brain

Current first-line treatments for major depressive disorder (MDD) include pharmacotherapy and cognitive-behavioral therapy. However, one-third of depressed patients do not achieve remission after multiple medication trials, and psychotherapy can be costly and time-consuming. Although non-implantable neuromodulation (NIN) techniques such as transcranial magnetic stimulation, transcranial direct current stimulation, electroconvulsive therapy, and magnetic seizure therapy are gaining momentum for treating MDD, the efficacy of non-convulsive techniques is still modest, whereas use of convulsive modalities is limited by their cognitive side effects. In this context, we propose that NIN techniques could benefit from a precision-oriented approach. In this review, we discuss the challenges and opportunities in implementing such a framework, focusing on enhancing NIN effects via a combination of individualized cognitive interventions, using closed-loop approaches, identifying multimodal biomarkers, using computer electric field modeling to guide targeting and quantify dosage, and using machine learning algorithms to integrate data collected at multiple biological levels and identify clinical responders. Though promising, this framework is currently limited, as previous studies have employed small samples and did not sufficiently explore pathophysiological mechanisms associated with NIN response and side effects. Moreover, cost-effectiveness analyses have not been performed. Nevertheless, further advancements in clinical trials of NIN could shift the field toward a more “precision-oriented” practice.

Targeting brain functions from the scalp: Transcranial brain atlas based on large-scale fMRI data synthesis

Transcranial brain mapping techniques, such as functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS), have been playing an increasingly important role in studies of human brain functions. Given a brain function of interest, fNIRS probes and TMS coils should be properly placed on the scalp to ensure that the function is effectively measured or modulated. However, since brain activity is inside the skull and invisible to the researcher during placement, this blind targeting may cause the device to partially or completely miss the functional target, resulting in inconsistent experimental results and divergent clinical outcomes, especially when participants' structural MRI data are not available. To address this issue, we propose here a framework for targeting a designated function directly from the scalp. First, a functional brain atlas for the targeted brain function is constructed via a meta-analysis of large-scale functional magnetic resonance imaging datasets. Second, the functional brain atlas is presented on the scalp surface by using a transcranial mapping previously established from an structural MRI dataset (n ​= ​114), resulting in a novel functional transcranial brain atlas (fTBA). Finally, a low-cost, portable scalp-navigation system is used to localize the transcranial device on the individual's scalp with the guidance of the fTBA. To demonstrate the feasibility of the targeting framework, both fNIRS and TMS mapping experiments were conducted. The results show that fTBA-guided fNIRS positioning can detect functional activity with high sensitivity and specificity for working memory and motor systems; Moreover, compared with traditional TMS targeting approaches (e.g. the International 10-20 System and the conventional 5-cm rule), the fTBA suggested motor stimulation site is closesr to both the motor hotspot and the center of gravity of motor evoked potentials (MEP-COG). In summary, the proposed method unblinds the transcranial function targeting process using prior information, providing an effective and straightforward approach to transcranial brain mapping studies, especially those without participants' structural MRI data.

The physiological effects of noninvasive brain stimulation fundamentally differ across the human cortex

Transcranial magnetic stimulation (TMS) is a noninvasive method to modulate brain activity and behavior in humans. Still, stimulation effects substantially vary across studies and individuals, thereby restricting the large-scale application of TMS in research or clinical settings. We revealed that low-frequency stimulation had opposite impact on the functional connectivity of sensory and cognitive brain regions. Biophysical modeling then identified a neuronal mechanism underlying these region-specific effects. Stimulation of the frontal cortex decreased local inhibition and disrupted feedforward and feedback connections. Conversely, identical stimulation increased local inhibition and enhanced forward signaling in the occipital cortex. Last, we identified functional integration as a macroscale network parameter to predict the region-specific effect of stimulation in individual subjects. In summary, we revealed how TMS modulation critically depends on the connectivity profile of target regions and propose an imaging marker to improve sensitivity of noninvasive brain stimulation for research and clinical applications.

The road ahead in clinical network neuroscience

Clinical network neuroscience, the study of brain network topology in neurological and psychiatric diseases, has become a mainstay field within clinical neuroscience. Being a multidisciplinary group of clinical network neuroscience experts based in The Netherlands, we often discuss the current state of the art and possible avenues for future investigations. These discussions revolve around questions like "How do dynamic processes alter the underlying structural network?" and "Can we use network neuroscience for disease classification?" This opinion paper is an incomplete overview of these discussions and expands on ten questions that may potentially advance the field. By no means intended as a review of the current state of the field, it is instead meant as a conversation starter and source of inspiration to others.

Brain network disintegration as a final common pathway for delirium: a systematic review and qualitative meta-analysis

Delirium is an acute neuropsychiatric syndrome characterized by altered levels of attention and awareness with cognitive deficits. It is most prevalent in elderly hospitalized patients and related to poor outcomes. Predisposing risk factors, such as older age, determine the baseline vulnerability for delirium, while precipitating factors, such as use of sedatives, trigger the syndrome. Risk factors are heterogeneous and the underlying biological mechanisms leading to vulnerability for delirium are poorly understood. We tested the hypothesis that delirium and its risk factors are associated with consistent brain network changes. We performed a systematic review and qualitative meta-analysis and included 126 brain network publications on delirium and its risk factors. Findings were evaluated after an assessment of methodological quality, providing N=99 studies of good or excellent quality on predisposing risk factors, N=10 on precipitation risk factors and N=7 on delirium. Delirium was consistently associated with functional network disruptions, including lower EEG connectivity strength and decreased fMRI network integration. Risk factors for delirium were associated with lower structural connectivity strength and less efficient structural network organization. Decreased connectivity strength and efficiency appear to characterize structural brain networks of patients at risk for delirium, possibly impairing the functional network, while functional network disintegration seems to be a final common pathway for the syndrome.

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Delirium is consistently associated with functional network impairments.

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Risk factors are associated with lower structural connectivity strength.

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Risk factors are associated with a less efficient structural network organization.

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Structural impairments make the functional network more vulnerable to deterioration.

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Functional network disintegration seems to be a final common pathway for delirium.

Time to remission analysis for major depressive disorder after repetitive transcranial magnetic stimulation (rTMS)

We previously examined the efficacy of rTMS for major depressive disorder in an applied clinical practice. Clinical response was related to severity of depression as well as the rTMS instrument utilized suggesting a relationship to instrument or magnetic field parameters and individual factors. The effectiveness of repetitive transcranial magnetic stimulation (rTMS) in the treatment of major depressive disorder was further evaluated using Log-Rank statistics for time to remission outcomes. A follow-up retrospective medical records study was carried out on patients with major depressive disorder undergoing rTMS therapy at AwakeningsKC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. Cox Proportional Hazard with Log-Rank statistics were applied and the time course to clinical remission was evaluated over a 6-week period with respect to age, gender, and depression severity. Clinical response was observed referencing two different rTMS instruments (MagVenture; NeuroStar). Time to remission studies of 247 case reports (N=98 males; N=149 females) showed consistently greater clinically defined remission rates after 6 weeks of rTMS treatment for patients using the MagVenture vs NeuroStar instrument. Patients previously admitted for inpatient psychiatric hospitalization exhibited higher response rates when treated with the MagVenture rTMS unit. Stepwise Cox Proportional Hazards Regression final model of time to remission included rTMS unit, inpatient psychiatric hospitalization and obese body habitus. Response to rTMS in applied clinical practice is related to severity of psychiatric illness and may require consideration of magnetic field parameters of the rTMS unit with respect to individual factors such as sex or body composition.

Repetitive transcranial magnetic stimulation (rTMS) using different TMS instruments for major depressive disorder at a suburban tertiary clinic

Repetitive transcranial magnetic stimulation (rTMS) is a neurostimulatory technique used to modulate orbital frontal corticostriatal (OFC) activity and clinical symptomatology for psychiatric disorders involving OFC dysfunction. We examined the effectiveness of rTMS in the treatment of major depressive disorder in an applied clinical setting (Awakening KC CNI) to assess efficacy and optimize rTMS parameters within clinical practice. A retrospective review of medical records was carried out on patients with major depressive disorder undergoing rTMS therapy at Awakenings KC Clinical Neuroscience Institute (CNI), a suburban tertiary psychiatric clinic. A detailed de-identified data set of clinical outcomes was compiled. Patient Health Questionnaire 9 (PHQ-9) total score, clinical remission rate and week achieved were evaluated over 6 weeks of treatment to assess clinical response referencing two different rTMS instruments (MagVenture; NeuroStar). Our survey included 247 participants from males (N=98) and females (N=149) with average baseline PHQ-9 scores of 21.7±4, classified as severe depression. Clinically rated remission rates of 72% were achieved in 3.1±1.0 weeks and associated with prior history of psychiatric hospitalization, suicide attempts and substance use disorder. Average baseline PHQ- 9 scores decreased significantly over time with proportionately greater remission rates achieved for patients treated using the MagVenture over NeuroStar instrument. rTMS in applied clinical practice is efficacious over a wide range of settings and patients. Clinical response was related to severity of depression symptoms (e.g., prior hospitalization; suicide attempts) validating efficacy in critically ill groups. Clinical response may be impacted by rTMS instrument, magnetic field parameters or individual factors.

Transcranial magnetic stimulation in obsessive-compulsive disorder: A focus on network mechanisms and state dependence

Background

Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation technique that has shown promise as an adjunct treatment for the symptoms of Obsessive-Compulsive Disorder (OCD). Establishing a clear clinical role for TMS in the treatment of OCD is contingent upon evidence of significant efficacy and reliability in reducing symptoms.

Objectives

We present the basic principles supporting the effects of TMS on brain activity with a focus on network-based theories of brain function. We discuss the promises and pitfalls of this technique as a means of modulating brain activity and reducing OCD symptoms.

Methods

Synthesis of trends and critical perspective on the potential benefits and limitations of TMS interventions in OCD.

Findings

Our critical synthesis suggests the need to better quantify the role of TMS in a clinical setting. The context in which the stimulation is performed, the neural principles supporting the effects of local stimulation on brain networks, and the heterogeneity of neuroanatomy are often overlooked in the clinical application of TMS. The lack of consideration of these factors may partly explain the variable efficacy of TMS interventions for OCD symptoms.

Conclusions

Results from existing clinical studies and emerging knowledge about the effects of TMS on brain networks are encouraging but also highlight the need for further research into the use of TMS as a means of selectively normalising OCD brain network dynamics and reducing related symptoms. The combination of neuroimaging, computational modelling, and behavioural protocols known to engage brain networks affected by OCD has the potential to improve the precision and therapeutic efficacy of TMS interventions. The efficacy of this multimodal approach remains, however, to be established and its effective translation in clinical contexts presents technical and implementation challenges. Addressing these practical, scientific and technical issues is required to assess whether OCD can take its place alongside major depressive disorder as an indication for the use of TMS.