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

Same, Same but Different? A Multi-Method Review of the Processes Underlying Executive Control

Attention, working memory, and executive control are commonly considered distinct cognitive functions with important reciprocal interactions. Yet, longstanding evidence from lesion studies has demonstrated both overlap and dissociation in their behavioural expression and anatomical underpinnings, suggesting that a lower dimensional framework could be employed to further identify processes supporting goal-directed behaviour. Here, we describe the anatomical and functional correspondence between attention, working memory, and executive control by providing an overview of cognitive models, as well as recent data from lesion studies, invasive and non-invasive multimodal neuroimaging and brain stimulation. We emphasize the benefits of considering converging evidence from multiple methodologies centred on the identification of brain mechanisms supporting goal-driven behaviour. We propose that expanding on this approach should enable the construction of a comprehensive anatomo-functional framework with testable new hypotheses, and aid clinical neuroscience to intervene on impairments of executive functions.

Sleep deprivation changes frequency-specific functional organization of the resting human brain

Previous resting-state functional magnetic resonance imaging (rs-fMRI) studies have widely explored the temporal connection changes in the human brain following long-term sleep deprivation (SD). However, the frequency-specific topological properties of sleep-deprived functional networks remain virtually unclear. In this study, thirty-seven healthy male subjects underwent resting-state fMRI during rested wakefulness (RW) and after 36 hours of SD, and we examined frequency-specific spectral connection changes (0.01-0.08 Hz, interval = 0.01 Hz) caused by SD. First, we conducted a multivariate pattern analysis combining linear SVM classifiers with a robust feature selection algorithm, and the results revealed that accuracies of 74.29%-84.29% could be achieved in the classification between RW and SD states in leave-one-out cross-validation at different frequency bands, moreover, the spectral connection at the lowest and highest frequency bands exhibited higher discriminative power. Connection involving the cingulo-opercular network increased most, while connection involving the default-mode network decreased most following SD. Then we performed a graph-theoretic analysis and observed reduced low-frequency modularity and high-frequency global efficiency in the SD state. Moreover, hub regions, which were primarily situated in the cerebellum and the cingulo-opercular network after SD, exhibited high discriminative power in the aforementioned classification consistently. The findings may indicate the frequency-dependent effects of SD on the functional network topology and its efficiency of information exchange, providing new insights into the impact of SD on the human brain.

A precision functional atlas of personalized network topography and probabilities

Although the general location of functional neural networks is similar across individuals, there is vast person-to-person topographic variability. To capture this, we implemented precision brain mapping functional magnetic resonance imaging methods to establish an open-source, method-flexible set of precision functional network atlases-the Masonic Institute for the Developing Brain (MIDB) Precision Brain Atlas. This atlas is an evolving resource comprising 53,273 individual-specific network maps, from more than 9,900 individuals, across ages and cohorts, including the Adolescent Brain Cognitive Development study, the Developmental Human Connectome Project and others. We also generated probabilistic network maps across multiple ages and integration zones (using a new overlapping mapping technique, Overlapping MultiNetwork Imaging). Using regions of high network invariance improved the reproducibility of executive function statistical maps in brain-wide associations compared to group average-based parcellations. Finally, we provide a potential use case for probabilistic maps for targeted neuromodulation. The atlas is expandable to alternative datasets with an online interface encouraging the scientific community to explore and contribute to understanding the human brain function more precisely.

Frequency-specific segregation and integration of human cerebral cortex: An intrinsic functional atlas

The cognitive and behavioral functions of the human brain are supported by its frequency multiplexing mechanism. However, there is limited understanding of the dynamics of the functional network topology. This study aims to investigate the frequency-specific topology of the functional human brain using 7T rs-fMRI data. Frequency-specific parcellations were first performed, revealing frequency-dependent dynamics within the frontoparietal control, parietal memory, and visual networks. An intrinsic functional atlas containing 456 parcels was proposed and validated using stereo-EEG. Graph theory analysis suggested that, in addition to the task-positive vs. task-negative organization observed in static networks, there was a cognitive control system additionally from a frequency perspective. The reproducibility and plausibility of the identified hub sets were confirmed through 3T fMRI analysis, and their artificial removal had distinct effects on network topology. These results indicate a more intricate and subtle dynamics of the functional human brain and emphasize the significance of accurate topography.

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.

Estimating dynamic individual coactivation patterns based on densely sampled resting-state fMRI data and utilizing it for better subject identification

As a complex dynamic system, the brain exhibits spatially organized recurring patterns of activity over time. Coactivation patterns (CAPs), which analyzes data from each single frame, have been utilized to detect transient brain activity states recently. However, previous CAP analyses have been conducted at the group level, which might neglect meaningful individual differences. Here, we estimated individual CAP states at both subject- and scan-level based on a densely sampled dataset: Midnight Scan Club. We used differential identifiability, which measures the gap between intra- and inter-subject similarity, to evaluate individual differences. We found individual CAPs at the subject-level achieved the best fingerprinting ability by maintaining high intra-subject similarity and enlarging inter-subject differences, and brain regions of association networks mainly contributed to the identifiability. On the other hand, scan-level CAP states were unstable across scans for the same participant. Expectedly, we found subject-specific CAPs became more reliable and discriminative with more data (i.e., longer duration). As the acquisition time of each participant is limited in practice, our results recommend a data collection strategy that collects more scans with appropriate duration (e.g., 12 ~ 15 min/scan) to obtain more reliable subject-specific CAPs, when total acquisition time is fixed (e.g., 150 min). In summary, this work has constructed reliable subject-specific CAP states with meaningful individual differences, and recommended an appropriate data collection strategy, which can guide subsequent investigations into individualized brain dynamics.

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.

Individual-level functional connectomes predict the motor symptoms of Parkinson's disease

Abnormalities in functional connectivity networks are associated with sensorimotor networks in Parkinson's disease (PD) based on group-level mapping studies, but these results are controversial. Using individual-level cortical segmentation to construct individual brain atlases can supplement the individual information covered by group-level cortical segmentation. Functional connectivity analyses at the individual level are helpful for obtaining clinically useful markers and predicting treatment response. Based on the functional connectivity of individualized regions of interest, a support vector regression model was trained to estimate the severity of motor symptoms for each subject, and a correlation analysis between the estimated scores and clinical symptom scores was performed. Forty-six PD patients aged 50-75 years were included from the Parkinson's Progression Markers Initiative database, and 63 PD patients were included from the Beijing Rehabilitation Hospital database. Only patients below Hoehn and Yahr stage III were included. The analysis showed that the severity of motor symptoms could be estimated by the individualized functional connectivity between the visual network and sensorimotor network in early-stage disease. The results reveal individual-level connectivity biomarkers related to motor symptoms and emphasize the importance of individual differences in the prediction of the treatment response of PD.

Automated optimization of TMS coil placement for personalized functional network engagement

Transcranial magnetic stimulation (TMS) is used to treat multiple psychiatric and neurological conditions by manipulating activity in particular brain networks and circuits, but individual responses are highly variable. In clinical settings, TMS coil placement is typically based on either group average functional maps or scalp heuristics. Here, we found that this approach can inadvertently target different functional networks in depressed patients due to variability in their functional brain organization. More precise TMS targeting should be feasible by accounting for each patient's unique functional neuroanatomy. To this end, we developed a targeting approach, termed targeted functional network stimulation (TANS). The TANS approach improved stimulation specificity in silico in 8 highly sampled patients with depression and 6 healthy individuals and in vivo when targeting somatomotor functional networks representing the upper and lower limbs. Code for implementing TANS and an example dataset are provided as a resource.

Transcranial magnetic stimulation treatment in Alzheimer's disease: a meta-analysis of its efficacy as a function of protocol characteristics and degree of personalization

Alzheimer's disease (AD) represents the most common type of neurodegenerative disorder. Although our knowledge on the causes of AD remains limited and no curative treatments are available, several interventions have been proposed in trying to improve patients' symptomatology. Among those, transcranial magnetic stimulation (TMS) has been shown a promising, safe and noninvasive intervention to improve global cognitive functioning. Nevertheless, we currently lack agreement between research studies on the optimal stimulation protocol yielding the highest efficacy in these patients. To answer this query, we conducted a systematic literature search in PubMed, PsycINFO and Scopus databases and meta-analysis of studies published in the last 10 years (2010-2021) according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Differently from prior published meta-analytic work, we investigated whether protocols that considered participants-specific neuroimaging scans for the selection of individualized stimulation targets held more successful outcomes compared to those relying on a generalized targeting selection criteria. We then compared the effect sizes of subsets of studies based on additional protocol characteristics (frequency, duration of intervention, number of stimulation sites, use of concomitant cognitive training and patients' educational level). Our results confirm TMS efficacy in improving global cognitive functioning in mild-to-moderate AD patients, but also highlight the flaws of current protocols characteristics, including a possible lack of sufficient personalization in stimulation protocols.

Connectivity impairment of cerebellar and sensorimotor connector hubs in Parkinson’s disease

Cognitive and movement processes involved integration of several large-scale brain networks. Central to these integrative processes are connector hubs, brain regions characterized by strong connections with multiple networks. Growing evidence suggests that many neurodegenerative and psychiatric disorders are associated with connector hub dysfunctions. Using a network metric called functional connectivity overlap ratio, we investigated connector hub alterations in Parkinson’s disease. Resting-state functional MRI data from 99 patients (male/female = 44/55) and 99 age- and sex-matched healthy controls (male/female = 39/60) participating in our cross-sectional study were used in the analysis. We have identified two sets of connector hubs, mainly located in the sensorimotor cortex and cerebellum, with significant connectivity alterations with multiple resting-state networks. Sensorimotor connector hubs have impaired connections primarily with primary processing (sensorimotor, visual), visuospatial, and basal ganglia networks, whereas cerebellar connector hubs have impaired connections with basal ganglia and executive control networks. These connectivity alterations correlated with patients’ motor symptoms. Specifically, values of the functional connectivity overlap ratio of the cerebellar connector hubs were associated with tremor score, whereas that of the sensorimotor connector hubs with postural instability and gait disturbance score, suggesting potential association of each set of connector hubs with the disorder’s two predominant forms, the akinesia/rigidity and resting tremor subtypes. In addition, values of the functional connectivity overlap ratio of the sensorimotor connector hubs were highly predictive in classifying patients from controls with an accuracy of 75.76%. These findings suggest that, together with the basal ganglia, cerebellar and sensorimotor connector hubs are significantly involved in Parkinson’s disease with their connectivity dysfunction potentially driving the clinical manifestations typically observed in this disorder.

Person-specific and precision neuroimaging: Current methods and future directions

Most neuroimaging studies of brain function analyze data in normalized space to identify regions of common activation across participants. These studies treat interindividual differences in brain organization as noise, but this approach can obscure important information about the brain's functional architecture. Recently, a number of studies have adopted a person-specific approach that aims to characterize these individual differences and explore their reliability and implications for behavior. A subset of these studies has taken a precision imaging approach that collects multiple hours of data from each participant to map brain function on a finer scale. In this review, we provide a broad overview of how person-specific and precision imaging techniques have used resting-state measures to examine individual differences in the brain's organization and their impact on behavior, followed by how task-based activity continues to add detail to these discoveries. We argue that person-specific and precision approaches demonstrate substantial promise in uncovering new details of the brain's functional organization and its relationship to behavior in many areas of cognitive neuroscience. We also discuss some current limitations in this new field and some new directions it may take.

Graph Ricci curvatures reveal atypical functional connectivity in autism spectrum disorder

While standard graph-theoretic measures have been widely used to characterize atypical resting-state functional connectivity in autism spectrum disorder (ASD), geometry-inspired network measures have not been applied. In this study, we apply Forman–Ricci and Ollivier–Ricci curvatures to compare networks of ASD and typically developing individuals (N = 1112) from the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset. We find brain-wide and region-specific ASD-related differences for both Forman–Ricci and Ollivier–Ricci curvatures, with region-specific differences concentrated in Default Mode, Somatomotor and Ventral Attention networks for Forman–Ricci curvature. We use meta-analysis decoding to demonstrate that brain regions with curvature differences are associated to those cognitive domains known to be impaired in ASD. Further, we show that brain regions with curvature differences overlap with those brain regions whose non-invasive stimulation improves ASD-related symptoms. These results suggest the utility of graph Ricci curvatures in characterizing atypical connectivity of clinically relevant regions in ASD and other neurodevelopmental disorders.

The Beneficial Effects of Combining Anti-Aβ Antibody NP106 and Curcumin Analog TML-6 on the Treatment of Alzheimer's Disease in APP/PS1 Mice

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a multifactorial etiology. A multitarget treatment that modulates multifaceted biological functions might be more effective than a single-target approach. Here, the therapeutic efficacy of combination treatment using anti-Aβ antibody NP106 and curcumin analog TML-6 versus monotherapy was investigated in an APP/PS1 mouse model of AD. Our data demonstrate that both combination treatment and monotherapy attenuated brain Aβ and improved the nesting behavioral deficit to varying degrees. Importantly, the combination treatment group had the lowest Aβ levels, and insoluble forms of Aβ were reduced most effectively. The nesting performance of APP/PS1 mice receiving combination treatment was better than that of other APP/PS1 groups. Further findings indicate that enhanced microglial Aβ phagocytosis and lower levels of proinflammatory cytokines were concurrent with the aforementioned effects of NP106 in combination with TML-6. Intriguingly, combination treatment also normalized the gut microbiota of APP/PS1 mice to levels resembling the wild-type control. Taken together, combination treatment outperformed NP106 or TML-6 monotherapy in ameliorating Aβ pathology and the nesting behavioral deficit in APP/PS1 mice. The superior effect might result from a more potent modulation of microglial function, cerebral inflammation, and the gut microbiota. This innovative treatment paradigm confers a new avenue to develop more efficacious AD treatments.

Stimulating Memory: Reviewing Interventions Using Repetitive Transcranial Magnetic Stimulation to Enhance or Restore Memory Abilities

Human memory systems are imperfect recording devices that are affected by age and disease, but recent findings suggest that the functionality of these systems may be modifiable through interventions using non-invasive brain stimulation such as repetitive transcranial magnetic stimulation (rTMS). The translational potential of these rTMS interventions is clear: memory problems are the most common cognitive complaint associated with healthy aging, while pathological conditions such as Alzheimer's disease are often associated with severe deficits in memory. Therapies to improve memory or treat memory loss could enhance independence while reducing costs for public health systems. Despite this promise, several important factors limit the generalizability and translational potential of rTMS interventions for memory. Heterogeneity of protocol design, rTMS parameters, and outcome measures present significant challenges to interpretation and reproducibility. However, recent advances in cognitive neuroscience, including rTMS approaches and recent insights regarding functional brain networks, may offer methodological tools necessary to design new interventional studies with enhanced experimental rigor, improved reproducibility, and greater likelihood of successful translation to clinical settings. In this review, we first discuss the current state of the literature on memory modulation with rTMS, then offer a commentary on developments in cognitive neuroscience that are relevant to rTMS interventions, and finally close by offering several recommendations for the design of future investigations using rTMS to modulate human memory performance.

Combining transcranial magnetic stimulation with functional magnetic resonance imaging for probing and modulating neural circuits relevant to affective disorders

Combining transcranial magnetic stimulation (TMS) with functional magnetic resonance imaging offers an unprecedented tool for studying how brain networks interact in vivo and how repetitive trains of TMS modulate those networks among patients diagnosed with affective disorders. TMS compliments neuroimaging by allowing the interrogation of causal control among brain circuits. Together with TMS, neuroimaging can provide valuable insight into the mechanisms underlying treatment effects and downstream circuit communication. Here we provide a background of the method, review relevant study designs, consider methodological and equipment options, and provide statistical recommendations. We conclude by describing emerging approaches that will extend these tools into exciting new applications. This article is categorized under: Psychology > Emotion and Motivation Psychology > Theory and Methods Neuroscience > Clinical Neuroscience.

Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus-the default mode network (DMN) and a "medial temporal network" (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a "posterior medial" (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an "anterior temporal" (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a "medial prefrontal" (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

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.

Optimization of Surface Electromyography-Based Neurofeedback Rehabilitation Intervention System

In this paper, we study the effects of the neurofeedback method of surface EMG on electrophysiology and evaluate its effects on postural control, balance, and motor function using relevant scales. We optimize the neurofeedback rehabilitation intervention system based on surface EMG, study the objective assessment of neurofeedback rehabilitation intervention of surface EMG, and initially try to apply mirror therapy to the treatment of surface EMG. According to the different treatment methods, they were divided into the drug-only group, drug combined with electroacupuncture group, drug combined with facial muscle function training group, and drug combined with electroacupuncture combined with facial muscle function training group. Starting from the 10th day of the disease course, a course of 15 days contains three courses of treatment with a 3-day break for each course. Patients were tested on day 10, day 25, and day 40 of the disease course and the results of each test were recorded and analyzed. The results of each test were recorded and analyzed. The efficacy of four different methods for simple neurofeedback rehabilitation was compared according to the mean ratio of the root mean square of the patient's affected and healthy sides. The close relationship between surface EMG neurofeedback rehabilitation intervention and rehabilitation efficacy was also investigated, and the effect of different feedback modes on neurofeedback rehabilitation intervention was explored for the neurofeedback protocol and whether the use of the optimized neurorehabilitation protocol could achieve improved efficacy and have a sustained effect. The study showed that neurofeedback training interventions based on optimized surface EMG can achieve good long-term results, as demonstrated by improved postural control, balance, and motor function of patients; optimized neurofeedback rehabilitation intervention systems; and guiding physicians or nurses to work more effective clinically.

The role of transcranial magnetic stimulation in understanding attention-related networks in single subjects

Attention is a cognitive mechanism that has been studied through several methodological viewpoints, including animal models, MRI in stroke patients, and fMRI in healthy subjects. Activation-based fMRI research has also pointed to specific networks that activate during attention tasks. Most recently, network neuroscience has been used to study the functional connectivity of large-scale networks for attention to reveal how strongly correlated networks are to each other when engaged in specific behaviors. While neuroimaging has revealed important information about the neural correlates of attention, it is crucial to better understand how these processes are organized and executed in the brain in single subjects to guide theories and treatments for attention. Noninvasive brain stimulation is an effective tool to causally manipulate neural activity to detect the causal roles of circuits in behavior. We describe how combining transcranial magnetic stimulation (TMS) with modern precision network analysis in single-subject neuroimaging could test the roles of regions, circuits, and networks in regulating attention as a pathway to improve treatment effect magnitudes and specificity.

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Though studied for over 100 years, the brain basis of attention is still queried.

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Complexity in frameworks for attention makes brain mapping difficult.

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Relevant brain networks vary significantly across subjects, challenging progress.

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Single-subject neuroimaging with TMS can improve our understanding of attention.

Rapid Precision Functional Mapping of Individuals Using Multi-Echo fMRI

Resting-state functional magnetic resonance imaging (fMRI) is widely used in cognitive and clinical neuroscience, but long-duration scans are currently needed to reliably characterize individual differences in functional connectivity (FC) and brain network topology. In this report, we demonstrate that multi-echo fMRI can improve the reliability of FC-based measurements. In four densely sampled individual humans, just 10 min of multi-echo data yielded better test-retest reliability than 30 min of single-echo data in independent datasets. This effect is pronounced in clinically important brain regions, including the subgenual cingulate, basal ganglia, and cerebellum, and is linked to three biophysical signal mechanisms (thermal noise, regional variability in the rate of T₂* decay, and S₀-dependent artifacts) with spatially distinct influences. Together, these findings establish the potential utility of multi-echo fMRI for rapid precision mapping using experimentally and clinically tractable scan times and will facilitate longitudinal neuroimaging of clinical populations.

Lynch et al. demonstrate that the test-retest reliability of resting-state connectivity measurements can be improved using multi-echo fMRI. This effect is pronounced in clinically important brain regions and could help facilitate precision mapping of functional brain networks in healthy people and patient populations.

Callosal anisotropy predicts attentional network changes after parietal inhibitory stimulation

Hemispatial neglect is thought to result from disruption of interhemispheric equilibrium. Right hemisphere lesions deactivate the right frontoparietal network and hyperactivate the left via release from interhemispheric inhibition. Support for this putative mechanism comes from neuropsychological evidence as well as transcranial magnetic stimulation (TMS) studies in healthy subjects, in whom right posterior parietal cortex (PPC) inhibition causes neglect-like, rightward, visuospatial bias. Concurrent TMS and fMRI after right PPC TMS show task-dependent changes but may fail to identify effects of stimulation in areas not directly activated by the specific task, complicating interpretations. We used resting-state functional connectivity (RSFC) after inhibitory TMS over the right PPC to examine changes in the networks underlying visuospatial attention and used diffusion-weighted imaging to measure the structural properties of relevant white matter pathways. In a crossover experiment in healthy individuals, we delivered continuous theta burst TMS to the right PPC and vertex as control condition. We hypothesized that PPC inhibitory stimulation would result in a rightward visuospatial bias, decrease frontoparietal RSFC, and increase the PPC RSFC with the attentional network in the left hemisphere. We also expected that individual differences in fractional anisotropy (FA) of the frontoparietal network and the callosal pathway between the PPCs would account for variability of the TMS-induced RSFC changes. As hypothesized, TMS over the right PPC caused a rightward shift in line bisection judgment and increased RSFC between the right PPC and the left superior temporal gyrus. This effect was inversely related to FA in the posterior corpus callosum. Local inhibition of the right PPC reshapes connectivity in the attentional network and depends significantly on interhemispheric connections.

Probing rapid network reorganization of motor and language functions via neuromodulation and neuroimaging

Motor and cognitive functions are organized in large-scale networks in the human brain that interact to enable flexible adaptation of information exchange to ever-changing environmental conditions. In this review, we discuss the unique potential of the consecutive combination of repetitive transcranial magnetic stimulation (rTMS) and functional neuroimaging to probe network organization and reorganization in the healthy and lesioned brain. First, we summarize findings highlighting the flexible (re-)distribution and short-term reorganization in motor and cognitive networks in the healthy brain. Plastic after-effects of rTMS result in large-scale changes on the network level affecting both local and remote activity within the stimulated network as well as interactions between the stimulated and distinct functional networks. While the number of combined rTMS-fMRI studies in patients with brain lesions remains scarce, preliminary evidence suggests that the lesioned brain flexibly (re-)distributes its computational capacities to functionally reorganize impaired brain functions, using a similar set of mechanisms to achieve adaptive network plasticity compared to short-term reorganization observed in the healthy brain after rTMS. In general, both short-term reorganization in the healthy brain and stroke-induced reorganization seem to rely on three general mechanisms of adaptive network plasticity that allow to maintain and recover function: i) interhemispheric changes, including increased contribution of homologous regions in the contralateral hemisphere and increased interhemispheric connectivity, ii) increased interactions between differentially specialized networks and iii) increased contributions of domain-general networks after disruption of more specific functions. These mechanisms may allow for computational flexibility of large-scale neural networks underlying motor and cognitive functions. Future studies should use complementary approaches to address the functional relevance of adaptive network plasticity and further delineate how these general mechanisms interact to enable network flexibility. Besides furthering our neurophysiological insights into brain network interactions, identifying approaches to support and enhance adaptive network plasticity may result in clinically relevant diagnostic and treatment approaches.

Understanding diaschisis models of attention dysfunction with rTMS

Visual attentive tracking requires a balance of excitation and inhibition across large-scale frontoparietal cortical networks. Using methods borrowed from network science, we characterize the induced changes in network dynamics following low frequency (1 Hz) repetitive transcranial magnetic stimulation (rTMS) as an inhibitory noninvasive brain stimulation protocol delivered over the intraparietal sulcus. When participants engaged in visual tracking, we observed a highly stable network configuration of six distinct communities, each with characteristic properties in node dynamics. Stimulation to parietal cortex had no significant impact on the dynamics of the parietal community, which already exhibited increased flexibility and promiscuity relative to the other communities. The impact of rTMS, however, was apparent distal from the stimulation site in lateral prefrontal cortex. rTMS temporarily induced stronger allegiance within and between nodal motifs (increased recruitment and integration) in dorsolateral and ventrolateral prefrontal cortex, which returned to baseline levels within 15 min. These findings illustrate the distributed nature by which inhibitory rTMS perturbs network communities and is preliminary evidence for downstream cortical interactions when using noninvasive brain stimulation for behavioral augmentations.

Personalizing neuromodulation

In the era of "big data", we are gaining rich person-specific information about neuroanatomy, neural function, and cognitive functions. However, the optimal ways to create precise approaches to optimize individuals' mental functions in health and disease are unclear. Multimodal analysis and modeling approaches can guide neuromodulation by combining anatomical networks, functional signal analysis, and cognitive neuroscience paradigms in single subjects. Our progress could be improved by progressing from statistical fits to mechanistic models. Using transcranial magnetic stimulation as an example, we discuss how integrating methods with a focus on mechanisms could improve our predictions TMS effects within individuals, refine our models of health and disease, and improve our treatments.

Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry

Studies comparing diverse groups have shown that many psychiatric diseases involve disruptions across distributed large-scale networks of the brain. There is hope that functional magnetic resonance imaging (fMRI) functional connectivity techniques will shed light on these disruptions, providing prognostic and diagnostic biomarkers as well as targets for therapeutic interventions. However, to date, progress on clinical translation of fMRI methods has been limited. Here, we argue that this limited translation is driven by a combination of intersubject heterogeneity and the relatively low reliability of standard fMRI techniques at the individual level. We review a potential solution to these limitations: the use of new "precision" fMRI approaches that shift the focus of analysis from groups to single individuals through the use of extended data acquisition strategies. We begin by discussing the potential advantages of fMRI functional connectivity methods for improving our understanding of functional neuroanatomy and disruptions in psychiatric disorders. We then discuss the budding field of precision fMRI and findings garnered from this work. We demonstrate that precision fMRI can improve the reliability of functional connectivity measures, while showing high stability and sensitivity to individual differences. We close by discussing the application of these approaches to clinical settings.

Three Distinct Sets of Connector Hubs Integrate Human Brain Function

Control over behavior is enabled by the brain's control networks, which interact with lower-level sensory motor and default networks to regulate their functions. Such interactions are facilitated by specialized "connector hub" regions that interconnect discrete networks. Previous work has treated hubs as a single category of brain regions, although their unitary nature is dubious when examined in individual brains. Here we investigated the nature of hubs by using fMRI to characterize individual-specific hub regions in two independent datasets. We identified three separable sets of connector hubs that integrate information between specific brain networks. These three hub categories occupy different positions within the brain's network structure; they affect networks differently when artificially lesioned, and they are differentially engaged during cognitive and motor task performance. This work suggests a model of brain organization in which different connector hubs integrate control functions and enable top-down control of separate processing streams.

A Taxonomy of Brain-Behavior Relationships After Stroke

Purpose Understanding the brain basis of language and cognitive outcomes is a major goal of aphasia research. Prior studies have not often considered the many ways that brain features can relate to behavioral outcomes or the mechanisms underlying these relationships. The purpose of this review article is to provide a new framework for understanding the ways that brain features may relate to language and cognitive outcomes from stroke. Method Brain-behavior relationships that may be important for aphasia outcomes are organized into a taxonomy, including features of the lesion and features of brain tissue spared by the lesion. Features of spared brain tissue are categorized into those that change after stroke and those that do not. Features that change are further subdivided, and multiple mechanisms of brain change after stroke are discussed. Results Features of the stroke, including size, location, and white matter damage, relate to many behavioral outcomes and likely account for most of the variance in outcomes. Features of the spared brain tissue that are unchanged by stroke, such as prior ischemic disease in the white matter, contribute to outcomes. Many different neurobiological and behavioral mechanisms may drive changes in the brain after stroke in association with behavioral recovery. Changes primarily driven by neurobiology are likely to occur in brain regions with a systematic relationship to the stroke distribution. Changes primarily driven by behavior are likely to occur in brain networks related to the behavior driving the change. Conclusions Organizing the various hypothesized brain-behavior relationships according to this framework and considering the mechanisms that drive these relationships may help investigators develop specific experimental designs and more complete statistical models to explain language and cognitive abilities after stroke. Eight main recommendations for future research are provided. Presentation Video https://doi.org/10.23641/asha.10257578.