Individualized functional targeting for rTMS: A powerful idea whose time has come?

The promise of precision functional mapping for neuroimaging in psychiatry

Precision functional mapping (PFM) is a neuroimaging approach to reliably estimate metrics of brain function from individual people via the collection of large amounts of fMRI data (hours per person). This method has revealed much about the inter-individual variation of functional brain networks. While standard group-level studies, in which we average brain measures across groups of people, are important in understanding the generalizable neural underpinnings of neuropsychiatric disorders, many disorders are heterogeneous in nature. This heterogeneity often complicates clinical care, leading to patient uncertainty when considering prognosis or treatment options. We posit that PFM methods may help streamline clinical care in the future, fast-tracking the choice of personalized treatment that is most compatible with the individual. In this review, we provide a history of PFM studies, foundational results highlighting the benefits of PFM methods in the pursuit of an advanced understanding of individual differences in functional network organization, and possible avenues where PFM can contribute to clinical translation of neuroimaging research results in the way of personalized treatment in psychiatry.

White matter predicts tDCS antidepressant effects in a sham-controlled clinical trial study

Transcranial direct current stimulation (tDCS) has been used as treatment for depression, but its effects are heterogeneous. We investigated, in a subsample of the clinical trial Escitalopram versus Electrical Direct Current Therapy for Depression Study (ELECTTDCS), whether white matter areas associated with depression disorder were associated with tDCS response. Baseline diffusion tensor imaging data were analyzed from 49 patients (34 females, mean age 41.9) randomized to escitalopram 20 mg/day, tDCS (2 mA, 30 min, 22 sessions), or placebo. Antidepressant outcomes were assessed by Hamilton Depression Rating Scale-17 (HDRS) after 10-week treatment. We used whole-brain tractography for extracting white matter measures for anterior corpus callosum, and bilaterally for cingulum bundle, striato-frontal, inferior occipito-frontal fasciculus and uncinate. For the rostral body, tDCS group showed higher MD associated with antidepressant effects (estimate = -5.13 ± 1.64, p = 0.002), and tDCS significantly differed from the placebo and the escitalopram group. The left striato-frontal tract showed higher FA associated with antidepressant effects (estimate = -2.14 ± 0.72, p = 0.003), and tDCS differed only from the placebo group. For the right uncinate, the tDCS group lower AD values were associated with higher HDRS decrease (estimate = -1.45 ± 0.67, p = 0.031). Abnormalities in white matter MDD-related areas are associated with tDCS antidepressant effects. Suggested better white matter microstructure of the left prefrontal cortex was associated with tDCS antidepressant effects. Future studies should investigate whether these findings are driven by electric field diffusion and density in these areas.

Effects of neuromodulation on cognitive and emotional responses to psychosocial stressors in healthy humans

Physiological and psychological stressors can exert wide-ranging effects on the human brain and behavior. Research has improved understanding of how the sympatho-adreno-medullary (SAM) and hypothalamic-pituitary-adrenocortical (HPA) axes respond to stressors and the differential responses that occur depending on stressor type. Although the physiological function of SAM and HPA responses is to promote survival and safety, exaggerated psychobiological reactivity can occur in psychiatric disorders. Exaggerated reactivity may occur more for certain types of stressors, specifically, psychosocial stressors. Understanding stressor effects and how the body regulates these responses can provide insight into ways that psychobiological reactivity can be modulated. Non-invasive neuromodulation is one way that responding to stressors may be altered; research into these interventions may provide further insights into the brain circuits that modulate stress reactivity. This review focuses on the effects of acute psychosocial stressors and how neuromodulation might be effective in altering stress reactivity. Although considerable research into stress interventions focuses on treating pathology, it is imperative to first understand these mechanisms in non-clinical populations; therefore, this review will emphasize populations with no known pathology and consider how these results may translate to those with psychiatric pathologies.

Personalizing Repetitive Transcranial Magnetic Stimulation Parameters for Depression Treatment Using Multimodal Neuroimaging

Repetitive transcranial magnetic stimulation (rTMS) is a tool that can be used to administer treatment for neuropsychiatric disorders such as major depressive disorder, although the clinical efficacy is still rather modest. Overly general stimulation protocols that consider neither patient-specific depression symptomology nor individualized brain characteristics, such as anatomy or structural and functional connections, may be the cause of the high inter- and intraindividual variability in rTMS clinical responses. Multimodal neuroimaging can provide the necessary insights into individual brain characteristics and can therefore be used to personalize rTMS parameters. Optimal coil positioning should include a three-step process: 1) identify the optimal (indirect) target area based on the exact symptom pattern of the patient; 2) derive the cortical (direct) target location based on functional and/or structural connectomes derived from functional and diffusion magnetic resonance imaging data; and 3) determine the ideal coil position by computational modeling, such that the electric field distribution overlaps with the cortical target. These TMS-induced electric field simulations, derived from anatomical and diffusion magnetic resonance imaging data, can be further applied to compute optimal stimulation intensities. In addition to magnetic resonance imaging, electroencephalography can provide complementary information regarding the ongoing brain oscillations. This information can be used to determine the optimal timing and frequency of the stimuli. The heightened benefits of these personalized stimulation approaches are logically reasoned, but speculative. Randomized clinical trials will be required to compare clinical responses from standard rTMS protocols to personalized protocols. Ultimately, an optimized clinical response may result from precision protocols derived from combinations of personalized stimulation parameters.

Mapping Inter-individual Functional Connectivity Variability in TMS Targets for Major Depressive Disorder

Transcranial magnetic stimulation (TMS) is an emerging alternative to existing treatments for major depressive disorder (MDD). The effects of TMS on both brain physiology and therapeutic outcomes are known to be highly variable from subject to subject, however. Proposed reasons for this variability include individual differences in neurophysiology, in cortical geometry, and in brain connectivity. Standard approaches to TMS target site definition tend to focus on coordinates or landmarks within the individual brain regions implicated in MDD, such as the dorsolateral prefrontal cortex (dlPFC) and orbitofrontal cortex (OFC). Additionally considering the network connectivity of these sites (i.e., the wider set of brain regions that may be mono- or poly-synaptically activated by TMS stimulation) has the potential to improve subject-specificity of TMS targeting and, in turn, improve treatment outcomes. In this study, we looked at the functional connectivity (FC) of dlPFC and OFC TMS targets, based on induced electrical field (E-field) maps, estimated using the SimNIBS library. We hypothesized that individual differences in spontaneous functional brain dynamics would contribute more to downstream network engagement than individual differences in cortical geometry (i.e., E-field variability). We generated individualized E-field maps on the cortical surface for 121 subjects (67 female) from the Human Connectome Project database using tetrahedral head models generated from T1- and T2-weighted MR images. F3 and Fp1 electrode positions were used to target the left dlPFC and left OFC, respectively. We analyzed inter-subject variability in the shape and location of these TMS target E-field patterns, their FC, and the major functional networks to which they belong. Our results revealed the key differences in TMS target FC between the dlPFC and OFC, and also how this connectivity varies across subjects. Three major functional networks were targeted across the dlPFC and OFC: the ventral attention, fronto-parietal and default-mode networks in the dlPFC, and the fronto-parietal and default mode networks in the OFC. Inter-subject variability in cortical geometry and in FC was high. Our analyses showed that the use of normative neuroimaging reference data (group-average or representative FC and subject E-field) allows prediction of which networks are targeted, but fails to accurately quantify the relative loading of TMS targeting on each of the principal networks. Our results characterize the FC patterns of canonical therapeutic TMS targets, and the key dimensions of their variability across subjects. The high inter-individual variability in cortical geometry and FC, leading to high variability in distributions of targeted brain networks, may account for the high levels of variability in physiological and therapeutic TMS outcomes. These insights should, we hope, prove useful as part of the broader effort by the psychiatry, neurology, and neuroimaging communities to help improve and refine TMS therapy, through a better understanding of the technology and its neurophysiological effects.

The MRi-Share database: brain imaging in a cross-sectional cohort of 1870 university students

We report on MRi-Share, a multi-modal brain MRI database acquired in a unique sample of 1870 young healthy adults, aged 18-35 years, while undergoing university-level education. MRi-Share contains structural (T1 and FLAIR), diffusion (multispectral), susceptibility-weighted (SWI), and resting-state functional imaging modalities. Here, we described the contents of these different neuroimaging datasets and the processing pipelines used to derive brain phenotypes, as well as how quality control was assessed. In addition, we present preliminary results on associations of some of these brain image-derived phenotypes at the whole brain level with both age and sex, in the subsample of 1722 individuals aged less than 26 years. We demonstrate that the post-adolescence period is characterized by changes in both structural and microstructural brain phenotypes. Grey matter cortical thickness, surface area and volume were found to decrease with age, while white matter volume shows increase. Diffusivity, either radial or axial, was found to robustly decrease with age whereas fractional anisotropy only slightly increased. As for the neurite orientation dispersion and densities, both were found to increase with age. The isotropic volume fraction also showed a slight increase with age. These preliminary findings emphasize the complexity of changes in brain structure and function occurring in this critical period at the interface of late maturation and early ageing.