Causal mapping of human brain function

MPGAN: Multi Pareto Generative Adversarial Network for the denoising and quantitative analysis of low-dose PET images of human brain

Positron emission tomography (PET) imaging is widely used in medical imaging for analyzing neurological disorders and related brain diseases. Usually, full-dose imaging for PET ensures image quality but raises concerns about potential health risks of radiation exposure. The contradiction between reducing radiation exposure and maintaining diagnostic performance can be effectively addressed by reconstructing low-dose PET (L-PET) images to the same high-quality as full-dose (F-PET). This paper introduces the Multi Pareto Generative Adversarial Network (MPGAN) to achieve 3D end-to-end denoising for the L-PET images of human brain. MPGAN consists of two key modules: the diffused multi-round cascade generator (GDmc) and the dynamic Pareto-efficient discriminator (DPed), both of which play a zero-sum game for n(n∈1,2,3) rounds to ensure the quality of synthesized F-PET images. The Pareto-efficient dynamic discrimination process is introduced in DPed to adaptively adjust the weights of sub-discriminators for improved discrimination output. We validated the performance of MPGAN using three datasets, including two independent datasets and one mixed dataset, and compared it with 12 recent competing models. Experimental results indicate that the proposed MPGAN provides an effective solution for 3D end-to-end denoising of L-PET images of the human brain, which meets clinical standards and achieves state-of-the-art performance on commonly used metrics.

Enhancing causal inference in population-based neuroimaging data in children and adolescents

Recent years have seen the increasing availability of large, population-based, longitudinal neuroimaging datasets, providing unprecedented capacity to examine brain-behavior relationships in the neurodevelopmental context. However, the ability of these datasets to deliver causal insights into brain-behavior relationships relies on the application of purpose-built analysis methods to counter the biases that otherwise preclude causal inference from observational data. Here we introduce these approaches (i.e., propensity score-based methods, the 'G-methods', targeted maximum likelihood estimation, and causal mediation analysis) and conduct a review to determine the extent to which they have been applied thus far in the field of developmental cognitive neuroscience. We identify just eight relevant studies, most of which employ propensity score-based methods. Many approaches are entirely absent from the literature, particularly those that promote causal inference in settings with complex, multi-wave data and repeated neuroimaging assessments. Causality is central to an etiological understanding of the relationship between the brain and behavior, as well as for identifying targets for prevention and intervention. Careful application of methods for causal inference may help the field of developmental cognitive neuroscience approach these goals.

Decoding the brain: From neural representations to mechanistic models

A central principle in neuroscience is that neurons within the brain act in concert to produce perception, cognition, and adaptive behavior. Neurons are organized into specialized brain areas, dedicated to different functions to varying extents, and their function relies on distributed circuits to continuously encode relevant environmental and body-state features, enabling other areas to decode (interpret) these representations for computing meaningful decisions and executing precise movements. Thus, the distributed brain can be thought of as a series of computations that act to encode and decode information. In this perspective, we detail important concepts of neural encoding and decoding and highlight the mathematical tools used to measure them, including deep learning methods. We provide case studies where decoding concepts enable foundational and translational science in motor, visual, and language processing.

Brain metabolic response to repetitive transcranial magnetic stimulation to lesion network in cervical dystonia

BACKGROUND

A previous study identified a brain network underlying cervical dystonia (CD) based on causal brain lesions. This network was shown to be abnormal in idiopathic CD and aligned with connections mediating treatment response to deep brain stimulation, suggesting generalizability across etiologies and relevance for treatment. The main nodes of this network were located in the deep cerebellar structures and somatosensory cortex (S1), the latter of which can be easily reached via non-invasive brain stimulation. To date, there are no studies testing brain stimulation to networks identified using lesion network mapping.

OBJECTIVES

To assess target engagement by stimulating the S1 and testing the brain's acute metabolic response to repetitive transcranial magnetic stimulation in CD patients and healthy controls.

METHODS

Thirteen CD patients and 14 controls received a single session of continuous theta burst (cTBS) and sham to the right S1. Changes in regional brain glucose metabolism were measured using [18F]FDG-PET.

RESULTS

cTBS increased metabolism at the stimulation site in CD (P = 0.03) but not in controls (P = 0.15; group difference P = 0.01). In subcortical regions, cTBS increased metabolism in the brainstem in CD only (PFDR = 0.04). The remote activation was positively associated with dystonia severity and efficacy of sensory trick phenomenon in CD patients.

CONCLUSIONS

Our results provide further evidence of abnormal sensory system function in CD and show that a single session of S1 cTBS is sufficient to induce measurable changes in brain glucose metabolism. These findings support target engagement, motivating therapeutic trials of cTBS to the S1 in CD.

Heterogenous brain activations across individuals localize to a common network

Task functional magnetic resonance imaging research has generally shielded away from studying individuals due to the low reproducibility. Here, we propose that heterogeneous brain activations across individuals localize to a common network. To test this hypothesis, we use working memory (WM) as our example. First, we showed that discrete-brain-based reproducibility of brain activation during WM across individuals was low. Then, we used activation network mapping (ANM) technique to identify each individual's brain network of WM and found that network-based reproducibility was rather high. Prediction analyses using machine learning algorithms indicated that individual WM networks identified via ANM can predict WM behavioral performance. This predictive ability even outperformed that of brain activations. Our study provides a new explanation on the low reproducibility of brain activations across individuals. The results suggest that ANM can be used to identify individual brain networks of cognitive processes, thus promising broad potential applications.

Mapping lesion-related human aggression to a common brain network

BACKGROUND

Aggression exacts a significant toll on human societies and is highly prevalent among neuropsychiatric patients for which neural mechanisms are unclear and treatment options are limited.

METHODS

Using recently validated lesion network mapping technique, we derived an aggression associated network by analyzing 182 patients who had suffered penetrating head injuries during their service in the Vietnam War. To test whether damage to this lesion-derived network would increase the risk of aggression related neuropsychiatric symptoms, we used the Harvard Lesion Repository (N = 928). To explore potential therapeutic relevance of this network, we used an independent Deep brain stimulation dataset of 25 patients with epilepsy, in which irritability and aggression are known potential side effects.

RESULTS

We found that lesions associated with aggression occurred in many different brain locations but were characterized by a specific brain network defined by functional connectivity to a hub region in the right prefrontal cortex (PFC). This network involves positive connectivity to the ventromedial PFC, dorsolateral PFC, frontal pole, posterior cingulate cortex, anterior cingulate cortex, temporal-parietal junction, and lateral temporal lobe and negative connectivity to the amygdala, hippocampus, insula, and visual cortex. Among all 25 neuropsychiatric symptoms included in the Harvard Lesion Repository, criminality demonstrated the most alignment with our aggression associated network. DBS site connectivity to this same network was associated with increased irritability.

CONCLUSIONS

We conclude that brain lesions associated with aggression map to a specific human brain circuit, and the functionally connected regions in this circuit provide testable targets for therapeutic neuromodulation.

Dynamics of heterogeneous Hopfield neural network with adaptive activation function based on memristor

Memristor and activation function are two important nonlinear factors of the memristive Hopfield neural network. The effects of different memristors on the dynamics of Hopfield neural networks have been studied by many researchers. However, less attention has been paid to the activation function. In this paper, we present a heterogeneous memristive Hopfield neural network with neurons using different activation functions. The activation functions include fixed activation functions and an adaptive activation function, where the adaptive activation function is based on a memristor. The theoretical and experimental study of the neural network's dynamics has been conducted using phase portraits, bifurcation diagrams, and Lyapunov exponents spectras. Numerical results show that complex dynamical behaviors such as multi-scroll chaos, transient chaos, state jumps and multi-type coexisting attractors can be observed in the heterogeneous memristive Hopfield neural network. In addition, the hardware implementation of memristive Hopfield neural network with adaptive activation function is designed and verified. The experimental results are in good agreement with those obtained using numerical simulations.

Symptomatology after damage to the angular gyrus through the lenses of modern lesion-symptom mapping

Brain-behavior relationships are complex. For instance, one might know a brain region's function(s) but still be unable to accurately predict deficit type or severity after damage to that region. Here, I discuss the case of damage to the angular gyrus (AG) that can cause left-right confusion, finger agnosia, attention deficit, and lexical agraphia, as well as impairment in sentence processing, episodic memory, number processing, and gesture imitation. Some of these symptoms are grouped under AG syndrome or Gerstmann's syndrome, though its exact underlying neuronal systems remain elusive. This review applies recent frameworks of brain-behavior modes and principles from modern lesion-symptom mapping to explain symptomatology after AG damage. It highlights four major issues for future studies: (1) functionally heterogeneous symptoms after AG damage need to be considered in terms of the degree of damage to (i) different subdivisions of the AG, (ii) different AG connectivity profiles that disconnect AG from distant regions, and (iii) lesion extent into neighboring regions damaged by the same infarct. (2) To explain why similar symptoms can also be observed after damage to other regions, AG damage needs to be studied in terms of the networks of regions that AG functions with, and other independent networks that might subsume the same functions. (3) To explain inter-patient variability on AG symptomatology, the degree of recovery-related brain reorganisation needs to account for time post-stroke, demographics, therapy input, and pre-stroke differences in functional anatomy. (4) A better integration of the results from lesion and functional neuroimaging investigations of AG function is required, with only the latter so far considering AG function in terms of a hub within the default mode network. Overall, this review discusses why it is so difficult to fully characterize the AG syndrome from lesion data, and how this might be addressed with modern lesion-symptom mapping.

Transcranial magnetic stimulation of the right inferior frontal gyrus impairs bilinguals' performance in language-switching tasks

It is widely accepted that bilinguals activate both languages simultaneously, even when intending to speak only one. A prevailing theory proposes that bilinguals inhibit the nontarget language to produce the target language, thought to be supported by evidence that the right inferior frontal gyrus (rIFG), a region typically associated with inhibition, is activated during language-switching tasks. However, it remains unclear whether the rIFG plays a causal or epiphenomenal role in this process. To explore the role of the rIFG, the present study employed transcranial magnetic stimulation (TMS) to modulate its neural activity and evaluate subsequent behavior in bilinguals. Specifically, twenty-nine Chinese-English bilinguals participated in the study and performed picture-naming tasks in single- and dual-language contexts after receiving sham stimulation (Sham), continuous theta burst stimulation (cTBS), or intermittent theta burst stimulation (iTBS) over the rIFG in three separate visits. Sham served as a control, with cTBS and iTBS intended to decrease and increase cortical excitability, respectively. We found that, compared to Sham, cTBS led to larger asymmetric switching costs and smaller asymmetric mixing costs, whereas iTBS resulted only in smaller asymmetric mixing costs. These findings suggest that cTBS targeting the rIFG likely impairs both local and global control. However, iTBS applied to the rIFG alone may not necessarily enhance language control mechanisms and could even hinder global control. Moreover, exploratory analyses found pronounced TMS-induced impairments in less balanced bilinguals, implying their potentially greater reliance on bilingual language control. Overall, this study is the first to suggest a causal role of the rIFG in language switching.

A potential target for noninvasive neuromodulation of PTSD symptoms derived from focal brain lesions in veterans

Neuromodulation trials for the treatment of posttraumatic stress disorder (PTSD) have yielded mixed results, and the optimal neuroanatomical target remains unclear. Here we analyzed three datasets to study brain circuitry causally linked to PTSD in military veterans. In veterans with penetrating traumatic brain injury, lesion locations that reduced probability of PTSD were preferentially connected to a circuit including the medial prefrontal cortex, amygdala and anterolateral temporal lobe. In veterans without lesions, PTSD was specifically associated with increased connectivity within this circuit. Reduced functional connectivity within this circuit after transcranial magnetic stimulation correlated with symptom reduction, even though the circuit was not directly targeted. This lesion-based 'PTSD circuit' may serve as a target for clinical trials of neuromodulation in veterans with PTSD.

Beyond the usual suspects: multi-factorial computational models in the search for neurodegenerative disease mechanisms

From Alzheimer's disease to amyotrophic lateral sclerosis, the molecular cascades underlying neurodegenerative disorders remain poorly understood. The clinical view of neurodegeneration is confounded by symptomatic heterogeneity and mixed pathology in almost every patient. While the underlying physiological alterations originate, proliferate, and propagate potentially decades before symptomatic onset, the complexity and inaccessibility of the living brain limit direct observation over a patient's lifespan. Consequently, there is a critical need for robust computational methods to support the search for causal mechanisms of neurodegeneration by distinguishing pathogenic processes from consequential alterations, and inter-individual variability from intra-individual progression. Recently, promising advances have been made by data-driven spatiotemporal modeling of the brain, based on in vivo neuroimaging and biospecimen markers. These methods include disease progression models comparing the temporal evolution of various biomarkers, causal models linking interacting biological processes, network propagation models reproducing the spatial spreading of pathology, and biophysical models spanning cellular- to network-scale phenomena. In this review, we discuss various computational approaches for integrating cross-sectional, longitudinal, and multi-modal data, primarily from large observational neuroimaging studies, to understand (i) the temporal ordering of physiological alterations, i(i) their spatial relationships to the brain's molecular and cellular architecture, (iii) mechanistic interactions between biological processes, and (iv) the macroscopic effects of microscopic factors. We consider the extents to which computational models can evaluate mechanistic hypotheses, explore applications such as improving treatment selection, and discuss how model-informed insights can lay the groundwork for a pathobiological redefinition of neurodegenerative disorders.

Low-intensity focused ultrasound to the insula differentially modulates the heartbeat-evoked potential: A proof-of-concept study

OBJECTIVE

The heartbeat evoked potential (HEP) is a brain response time-locked to the heartbeat and a potential marker of interoceptive processing that may be generated in the insula and dorsal anterior cingulate cortex (dACC). Low-intensity focused ultrasound (LIFU) can selectively modulate sub-regions of the insula and dACC to better understand their contributions to the HEP.

METHODS

Healthy participants (n = 16) received stereotaxically targeted LIFU to the anterior insula (AI), posterior insula (PI), dACC, or Sham at rest during continuous electroencephalography (EEG) and electrocardiography (ECG) recording on separate days. Primary outcome was HEP amplitudes. Relationships between LIFU pressure and HEP changes and effects of LIFU on heart rate and heart rate variability (HRV) were also explored.

RESULTS

Relative to sham, LIFU to the PI, but not AI or dACC, decreased HEP amplitudes; PI effects were partially explained by increased LIFU pressure. LIFU did not affect heart rate or HRV.

CONCLUSIONS

These results demonstrate the ability to modulate HEP amplitudes via non-invasive targeting of key interoceptive brain regions.

SIGNIFICANCE

Our findings have implications for the causal role of these areas in bottom-up heart-brain communication that could guide future work investigating the HEP as a marker of interoceptive processing in healthy and clinical populations.

A neural network for religious fundamentalism derived from patients with brain lesions

Religious fundamentalism, characterized by rigid adherence to a set of beliefs putatively revealing inerrant truths, is ubiquitous across cultures and has a global impact on society. Understanding the psychological and neurobiological processes producing religious fundamentalism may inform a variety of scientific, sociological, and cultural questions. Research indicates that brain damage can alter religious fundamentalism. However, the precise brain regions involved with these changes remain unknown. Here, we analyzed brain lesions associated with varying levels of religious fundamentalism in two large datasets from independent laboratories. Lesions associated with greater fundamentalism were connected to a specific brain network with nodes in the right orbitofrontal, dorsolateral prefrontal, and inferior parietal lobe. This fundamentalism network was strongly right hemisphere lateralized and highly reproducible across the independent datasets (r = 0.82) with cross-validations between datasets. To explore the relationship of this network to lesions previously studied by our group, we tested for similarities to twenty-one lesion-associated conditions. Lesions associated with confabulation and criminal behavior showed a similar connectivity pattern as lesions associated with greater fundamentalism. Moreover, lesions associated with poststroke pain showed a similar connectivity pattern as lesions associated with lower fundamentalism. These findings are consistent with the current understanding of hemispheric specializations for reasoning and lend insight into previously observed epidemiological associations with fundamentalism, such as cognitive rigidity and outgroup hostility.

Uncovering the determinants of brain functioning, behavior and their interplay in the light of context

Notwithstanding the huge progress in molecular and cellular neuroscience, our ability to understand the brain and develop effective treatments promoting mental health is still limited. This can be partially ascribed to the reductionist, deterministic and mechanistic approaches in neuroscience that struggle with the complexity of the central nervous system. Here, I introduce the Context theory of constrained systems proposing a novel role of contextual factors and genetic, molecular and neural substrates in determining brain functioning and behavior. This theory entails key conceptual implications. First, context is the main driver of behavior and mental states. Second, substrates, from genes to brain areas, have no direct causal link to complex behavioral responses as they can be combined in multiple ways to produce the same response and different responses can impinge on the same substrates. Third, context and biological substrates play distinct roles in determining behavior: context drives behavior, substrates constrain the behavioral repertoire that can be implemented. Fourth, since behavior is the interface between the central nervous system and the environment, it is a privileged level of control and orchestration of brain functioning. Such implications are illustrated through the Kitchen metaphor of the brain. This theoretical framework calls for the revision of key concepts in neuroscience and psychiatry, including causality, specificity and individuality. Moreover, at the clinical level, it proposes treatments inducing behavioral changes through contextual interventions as having the highest impact to reorganize the complexity of the human mind and to achieve a long-lasting improvement in mental health.

Chronometric TMS-fMRI of personalized left dorsolateral prefrontal target reveals state-dependency of subgenual anterior cingulate cortex effects

Transcranial magnetic stimulation (TMS) applied to a left dorsolateral prefrontal cortex (DLPFC) area with a specific connectivity profile to the subgenual anterior cingulate cortex (sgACC) has emerged as a highly effective non-invasive treatment option for depression. However, antidepressant outcomes demonstrate significant variability among therapy plans and individuals. One overlooked contributing factor is the individual brain state at the time of treatment. In this study we used interleaved TMS-fMRI to investigate the influence of brain state on acute TMS effects, both locally and remotely. TMS was performed during rest and during different phases of cognitive task processing. Twenty healthy participants were included in this study. In the first session, imaging data for TMS targeting were acquired, allowing for identification of individualized targets in the left DLPFC based on highest anti-correlation with the sgACC. The second session involved chronometric interleaved TMS-fMRI measurements, with 10 Hz triplets of TMS administered during rest and at distinct timings during an N-back task. Consistent with prior findings, interleaved TMS-fMRI revealed significant BOLD activation changes in the targeted network. The precise timing of TMS relative to the cognitive states during the task demonstrated distinct BOLD response in clinically relevant brain regions, including the sgACC. Employing a standardized timing approach for TMS using a task revealed more consistent modulation of the sgACC at the group level compared to stimulation during rest. In conclusion, our findings strongly suggest that acute local and remote effects of TMS are influenced by brain state during stimulation. This study establishes a basis for considering brain state as a significant factor in designing treatment protocols, possibly improving TMS treatment outcomes.

Centering cognitive neuroscience on task demands and generalization

Cognitive neuroscience seeks generalizable theories explaining the relationship between behavioral, physiological and mental states. In pursuit of such theories, we propose a theoretical and empirical framework that centers on understanding task demands and the mutual constraints they impose on behavior and neural activity. Task demands emerge from the interaction between an agent's sensory impressions, goals and behavior, which jointly shape the activity and structure of the nervous system on multiple spatiotemporal scales. Understanding this interaction requires multitask studies that vary more than one experimental component (for example, stimuli and instructions) combined with dense behavioral and neural sampling and explicit testing for generalization across tasks and data modalities. By centering task demands rather than mental processes that tasks are assumed to engage, this framework paves the way for the discovery of new generalizable concepts unconstrained by existing taxonomies, and moves cognitive neuroscience toward an action-oriented, dynamic and integrated view of the brain.

Brain growth until adolescence after a neonatal focal injury: sex related differences beyond lesion effect

Introduction

Early focal brain injuries lead to long-term disabilities with frequent cognitive impairments, suggesting global dysfunction beyond the lesion. While plasticity of the immature brain promotes better learning, outcome variability across individuals is multifactorial. Males are more vulnerable to early injuries and neurodevelopmental disorders than females, but long-term sex differences in brain growth after an early focal lesion have not been described yet. With this MRI longitudinal morphometry study of brain development after a Neonatal Arterial Ischemic Stroke (NAIS), we searched for differences between males and females in the trajectories of ipsi- and contralesional gray matter growth in childhood and adolescence, while accounting for lesion characteristics.

Methods

We relied on a longitudinal cohort (AVCnn) of patients with unilateral NAIS who underwent clinical and MRI assessments at ages 7 and 16 were compared to age-matched controls. Non-lesioned volumes of gray matter (hemispheres, lobes, regions, deep structures, cerebellum) were extracted from segmented T1 MRI images at 7 (Patients: 23 M, 16 F; Controls: 17 M, 18 F) and 16 (Patients: 18 M, 11 F; Controls: 16 M, 15 F). These volumes were analyzed using a Linear Mixed Model accounting for age, sex, and lesion characteristics.

Results

Whole hemisphere volumes were reduced at both ages in patients compared to controls (gray matter volume: -16% in males, -10% in females). In ipsilesional hemisphere, cortical gray matter and thalamic volume losses (average -13%) mostly depended on lesion severity, suggesting diaschisis, with minimal effect of patient sex. In the contralesional hemisphere however, we consistently found sex differences in gray matter volumes, as only male volumes were smaller than in male controls (average -7.5%), mostly in territories mirroring the contralateral lesion. Females did not significantly deviate from the typical trajectories of female controls. Similar sex differences were found in both cerebellar hemispheres.

Discussion

These results suggest sex-dependent growth trajectories after an early brain lesion with a contralesional growth deficit in males only. The similarity of patterns at ages 7 and 16 suggests that puberty has little effect on these trajectories, and that most of the deviation in males occurs in early childhood, in line with the well-described perinatal vulnerability of the male brain, and with no compensation thereafter.

Discovering the effective connectome of the brain with dynamic Bayesian DAG learning

Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.

Psychiatric neuroimaging designs for individualised, cohort, and population studies

Psychiatric neuroimaging faces challenges to rigour and reproducibility that prompt reconsideration of the relative strengths and limitations of study designs. Owing to high resource demands and varying inferential goals, current designs differentially emphasise sample size, measurement breadth, and longitudinal assessments. In this overview and perspective, we provide a guide to the current landscape of psychiatric neuroimaging study designs with respect to this balance of scientific goals and resource constraints. Through a heuristic data cube contrasting key design features, we discuss a resulting trade-off among small sample, precision longitudinal studies (e.g., individualised studies and cohorts) and large sample, minimally longitudinal, population studies. Precision studies support tests of within-person mechanisms, via intervention and tracking of longitudinal course. Population studies support tests of generalisation across multifaceted individual differences. A proposed reciprocal validation model (RVM) aims to recursively leverage these complementary designs in sequence to accumulate evidence, optimise relative strengths, and build towards improved long-term clinical utility.

Multimodal neuroimaging to characterize symptom-specific networks in movement disorders

Movement disorders, such as Parkinson's disease, essential tremor, and dystonia, are characterized by their predominant motor symptoms, yet diseases causing abnormal movement also encompass several other symptoms, including non-motor symptoms. Here we review recent advances from studies of brain lesions, neuroimaging, and neuromodulation that provide converging evidence on symptom-specific brain networks in movement disorders. Although movement disorders have traditionally been conceptualized as disorders of the basal ganglia, cumulative data from brain lesions causing parkinsonism, tremor and dystonia have now demonstrated that this view is incomplete. Several recent studies have shown that lesions causing a given movement disorder occur in heterogeneous brain locations, but disrupt common brain networks, which appear to be specific to each motor phenotype. In addition, findings from structural and functional neuroimaging in movement disorders have demonstrated that brain abnormalities extend far beyond the brain networks associated with the motor symptoms. In fact, neuroimaging findings in each movement disorder are strongly influenced by the constellation of patients' symptoms that also seem to map to specific networks rather than individual anatomical structures or single neurotransmitters. Finally, observations from deep brain stimulation have demonstrated that clinical changes, including both symptom improvement and side effects, are dependent on the modulation of large-scale networks instead of purely local effects of the neuromodulation. Combined, this multimodal evidence suggests that symptoms in movement disorders arise from distinct brain networks, encouraging multimodal imaging studies to better characterize the underlying symptom-specific mechanisms and individually tailor treatment approaches.

Macroscale traveling waves evoked by single-pulse stimulation of the human brain

Understanding the spatiotemporal dynamics of neural signal propagation is fundamental to unraveling the complexities of brain function. Emerging evidence suggests that cortico-cortical evoked potentials (CCEPs) resulting from single-pulse electrical stimulation may be used to characterize the patterns of information flow between and within brain networks. At present, the basic spatiotemporal dynamics of CCEP propagation cortically and subcortically are incompletely understood. We hypothesized that single-pulse electrical stimulation evokes neural traveling waves detectable in the three-dimensional space sampled by intracranial stereoelectroencephalography. Across a cohort of 21 adult patients with intractable epilepsy, we delivered 17,631 stimulation pulses and recorded CCEP responses in 1,019 electrode contacts. The distance between each pair of electrode contacts was approximated using three different metrics (Euclidean distance, path length, and geodesic distance), representing direct, tractographic, and transcortical propagation, respectively. For each robust CCEP, we extracted amplitude-, spectral-, and phase-based features to identify traveling waves emanating from the site of stimulation. Many evoked responses to stimulation appear to propagate as traveling waves (~14-28%), despite sparse sampling throughout the brain. These stimulation-evoked traveling waves exhibited biologically plausible propagation velocities (range 0.1-9.6 m/s). Our results reveal that direct electrical stimulation elicits neural activity with variable spatiotemporal dynamics, including the initiation of neural traveling waves.

Cross-regional coordination of activity in the human brain during autobiographical self-referential processing

For the human brain to operate, populations of neurons across anatomical structures must coordinate their activity within milliseconds. To date, our understanding of such interactions has remained limited. We recorded directly from the hippocampus (HPC), posteromedial cortex (PMC), ventromedial/orbital prefrontal cortex (OFC), and the anterior nuclei of the thalamus (ANT) during two experiments of autobiographical memory processing that are known from decades of neuroimaging work to coactivate these regions. In 31 patients implanted with intracranial electrodes, we found that the presentation of memory retrieval cues elicited a significant increase of low frequency (LF < 6 Hz) activity followed by cross-regional phase coherence of this LF activity before select populations of neurons within each of the four regions increased high-frequency (HF > 70 Hz) activity. The power of HF activity was modulated by memory content, and its onset followed a specific temporal order of ANT→HPC/PMC→OFC. Further, we probed cross-regional causal effective interactions with repeated electrical pulses and found that HPC stimulations cause the greatest increase in LF-phase coherence across all regions, whereas the stimulation of any region caused the greatest LF-phase coherence between that particular region and ANT. These observations support the role of the ANT in gating, and the HPC in synchronizing, the activity of cortical midline structures when humans retrieve self-relevant memories of their past. Our findings offer a fresh perspective, with high temporal fidelity, about the dynamic signaling and underlying causal connections among distant regions when the brain is actively involved in retrieving self-referential memories from the past.

Peaglet: A user-friendly probabilistic Kernel density estimation of intracranial cortical and subcortical stimulation sites

BACKGROUND

Data on human brain function obtained with direct electrical stimulation (DES) in neurosurgical patients have been recently integrated and combined with modern neuroimaging techniques, allowing a connectome-based approach fed by intraoperative DES data. Within this framework is crucial to develop reliable methods for spatial localization of DES-derived information to be integrated within the neuroimaging workflow.

NEW METHOD

To this aim, we applied the Kernel Density Estimation for modelling the distribution of DES sites from different patients into the MNI space. The algorithm has been embedded in a MATLAB-based User Interface, Peaglet. It allows an accurate probabilistic weighted and unweighted estimation of DES sites location both at cortical level, by using shortest path calculation along the brain 3D geometric topology, and subcortical level, by using a volume-based approach.

RESULTS

We applied Peaglet to investigate spatial estimation of cortical and subcortical stimulation sites provided by recent brain tumour studies. The resulting NIfTI maps have been anatomically investigated with neuroimaging open-source tools.

COMPARISON WITH EXISTING METHODS

Peaglet processes differently cortical and subcortical data following their distinguishing geometrical features, increasing anatomical specificity of DES-related results and their reliability within neuroimaging environments.

CONCLUSIONS

Peaglet provides a robust probabilistic estimation of the cortical and subcortical distribution of DES sites going beyond a region of interest approach, respecting cortical and subcortical intrinsic geometrical features. Results can be easily integrated within the neuroimaging workflow to drive connectomic analysis.

Linking Invasive and Noninvasive Brain Stimulation in Parkinson's Disease: A Randomized Trial

BACKGROUND

Recent imaging studies identified a brain network associated with clinical improvement following deep brain stimulation (DBS) in Parkinson's disease (PD), the PD response network.

OBJECTIVES

This study aimed to assess the impact of neuromodulation on PD motor symptoms by targeting this network noninvasively using multifocal transcranial direct current stimulation (tDCS).

METHODS

In a prospective, randomized, double-blinded, crossover trial, 21 PD patients (mean age 59.7 years, mean Hoehn & Yahr [H&Y] 2.4) received multifocal tDCS targeting the a-priori network. Twenty-minute sessions of tDCS and sham were administered on 2 days in randomized order. Movement Disorder Society-Unified Parkinson's Disease Rating Scale-Part III (MDS-UPDRS-III) scores were assessed.

RESULTS

Before intervention, MDS-UPDRS-III scores were comparable in both conditions (stimulation days: 37.38 (standard deviation [SD] = 12.50, confidence interval [CI] = 32.04, 42.73) vs. sham days: 36.95 (SD = 13.94, CI = 30.99, 42.91), P = 0.63). Active stimulation resulted in a reduction by 3.6 points (9.7%) to 33.76 (SD = 11.19, CI = 28.98, 38.55) points, whereas no relevant change was observed after sham stimulation (36.43 [SD = 14.15, CI = 30.38, 42.48], average improvement: 0.5 [1.4%]). Repeated-measures analysis of variance (ANOVA) confirmed significance (main effect of time: F(1,20)=4.35, P < 0.05). Tukey's post hoc tests indicated MDS-UPDRS-III improvement after active stimulation (t [20] = 2.9, P = 0.03) but not after sham (t [20] = 0.42, P > 0.05). In a subset of patients that underwent DBS surgery later, their DBS response correlated with tDCS effects (R = 0.55, P(1) = 0.04).

CONCLUSION

Noninvasive, multifocal tDCS targeting a DBS-derived network significantly improved PD motor symptoms. Despite a small effect size, this study provides proof of principle for the successful noninvasive neuromodulation of an invasively identified network. Future studies should investigate repeated tDCS sessions and their utility for screening before DBS surgery. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Dissociation of reading and naming in ventral occipitotemporal cortex

Lesions in the language-dominant ventral occipitotemporal cortex (vOTC) can result in selective impairment of either reading or naming, resulting in alexia or anomia. Yet, functional imaging studies that show differential activation for naming and reading do not reveal activity exclusively tuned to one of these inputs. To resolve this dissonance in the functional architecture of the vOTC, we used focused stimulation to the vOTC in 49 adult patients during reading and naming, and generated a population-level, probabilistic map to evaluate if reading and naming are clearly dissociable within individuals. Language mapping (50 Hz, 2829 stimulations) was performed during passage reading (216 positive sites) and visual naming (304 positive sites). Within the vOTC, we isolated sites that selectively disrupted reading (24 sites in 11 patients) or naming (27 sites in 12 patients), and those that disrupted both processes (75 sites in 21 patients). The anteromedial vOTC had a higher probability of producing naming disruption, while posterolateral regions resulted in greater reading-specific disruption. Between them lay a multi-modal region where stimulation disrupted both reading and naming. This work provides a comprehensive view of vOTC organization-the existence of a heteromodal cortex critical to both reading and naming, along with a causally dissociable unimodal naming cortex, and a reading-specific visual word form area in the vOTC. Their distinct roles as associative regions may thus relate to their connectivity within the broader language network that is disrupted by stimulation, more than to highly selective tuning properties. Our work also implies that pre-surgical mapping of both reading and naming is essential for patients requiring vOTC resections, as these functions are not co-localized, and such mapping may prevent the occurrence of unexpected deficits.

Is depression a global brain disorder with topographic dynamic reorganization?

Major depressive disorder (MDD) is characterized by a multitude of psychopathological symptoms including affective, cognitive, perceptual, sensorimotor, and social. The neuronal mechanisms underlying such co-occurrence of psychopathological symptoms remain yet unclear. Rather than linking and localizing single psychopathological symptoms to specific regions or networks, this perspective proposes a more global and dynamic topographic approach. We first review recent findings on global brain activity changes during both rest and task states in MDD showing topographic reorganization with a shift from unimodal to transmodal regions. Next, we single out two candidate mechanisms that may underlie and mediate such abnormal uni-/transmodal topography, namely dynamic shifts from shorter to longer timescales and abnormalities in the excitation-inhibition balance. Finally, we show how such topographic shift from unimodal to transmodal regions relates to the various psychopathological symptoms in MDD including their co-occurrence. This amounts to what we describe as 'Topographic dynamic reorganization' which extends our earlier 'Resting state hypothesis of depression' and complements other models of MDD.

A Comparative Study on Cognitive Assessment in Cerebellar and Supratentorial Stroke

This study aims to understand the cognitive profiles of cerebellar infarction patients and compare them to those with supratentorial infarctions, particularly frontal infarctions. This current study also aims to find reliable assessment tools for detecting cognitive impairment in cerebellar infarction patients. A total of fifty cerebellar infarction patients, sixty supratentorial infarction patients, and thirty-nine healthy controls were recruited. The Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Addenbrooke's Cognitive Examination III (ACE-III), and Cerebellar Cognitive Affective Syndrome scale (CCAS-s) were used to assess global cognitive function. An extensive neuropsychological assessment battery was also tested to evaluate the characteristics of each cognitive domain. To assess the features of cognitive function, a comprehensive neuropsychological evaluation tool was also utilized. The cerebral infarction patients demonstrated cognitive impairment comparable to those with frontal infarcts, notably characterized by disturbance in attention and executive function. However, the degree of cognitive impairment was comparatively milder in cerebellar infarction patients. Furthermore, the patients in the cerebellar group had worse scores in the ACE-III and CCAS-s compared to healthy controls. The two assessments also demonstrated a significant area under the curve values, indicating their effectiveness in distinguishing cognitive impairment in cerebellar infarctions. In conclusion, cognitive impairment in a cerebellar infarction resembles frontal lobe dysfunction but is generally mild. It can be accurately assessed using the ACE-III and CCAS-s scales.

State-Dependent tACS Effects Reveal the Potential Causal Role of Prestimulus Alpha Traveling Waves in Visual Contrast Detection

The intricate relationship between prestimulus alpha oscillations and visual contrast detection variability has been the focus of numerous studies. However, the causal impact of prestimulus alpha traveling waves on visual contrast detection remains largely unexplored. In our research, we sought to discern the causal link between prestimulus alpha traveling waves and visual contrast detection across different levels of mental fatigue. Using electroencephalography alongside a visual detection task with 30 healthy adults (13 females; 17 males), we identified a robust negative correlation between prestimulus alpha forward traveling waves (FTWs) and visual contrast threshold (VCT). Inspired by this correlation, we utilized 45/-45° phase-shifted transcranial alternating current stimulation (tACS) in a sham-controlled, double-blind, within-subject experiment with 33 healthy adults (23 females; 10 males) to directly modulate these alpha traveling waves. After the application of 45° phase-shifted tACS, we observed a substantial decrease in FTW and an increase in backward traveling waves, along with a concurrent increase in VCT, compared with the sham condition. These changes were particularly pronounced under a low fatigue state. The findings of state-dependent tACS effects reveal the potential causal role of prestimulus alpha traveling waves in visual contrast detection. Moreover, our study highlights the potential of 45/-45° phase-shifted tACS in cognitive modulation and therapeutic applications.

Lesions Causing Alice in Wonderland Syndrome Map to a Common Brain Network Linking Body and Size Perception

OBJECTIVE

Alice in Wonderland syndrome (AIWS) profoundly affects human perception of size and scale, particularly regarding one's own body and the environment. Its neuroanatomical basis has remained elusive, partly because brain lesions causing AIWS can occur in different brain regions. Here, we aimed to determine if brain lesions causing AIWS map to a distributed brain network.

METHODS

A retrospective case-control study analyzing 37 cases of lesion-induced AIWS identified through systematic literature review was conducted. Using resting-state functional connectome data from 1,000 healthy individuals, the whole-brain connections of each lesion were estimated and contrasted with those from a control dataset comprising 1,073 lesions associated with 25 other neuropsychiatric syndromes. Additionally, connectivity findings from lesion-induced AIWS cases were compared with functional neuroimaging results from 5 non-lesional AIWS cases.

RESULTS

AIWS-associated lesions were located in various brain regions with minimal overlap (≤33%). However, the majority of lesions (≥85%) demonstrated shared connectivity to the right extrastriate body area, known to be selectively activated by viewing body part images, and the inferior parietal cortex, involved in size and scale judgements. This pattern was uniquely characteristic of AIWS when compared with other neuropsychiatric disorders (family-wise error-corrected p < 0.05) and consistent with functional neuroimaging observations in AIWS due to nonlesional causes (median correlation r = 0.56, interquartile range 0.24).

INTERPRETATION

AIWS-related perceptual distortions map to one common brain network, encompassing regions critical for body representation and size-scale processing. These findings lend insight into the neuroanatomical localization of higher-order perceptual functions, and may inform future therapeutic strategies for perceptual disorders. ANN NEUROL 2024.

Proposal for a Mechanistic Disease Conceptualization in Clinical Neurosciences: The Neural Network Components (NNC) Model

ABSTRACT

Clinical neurosciences, and psychiatry specifically, have been challenged by the lack of a comprehensive and practical framework that explains the core mechanistic processes of variable psychiatric presentations. Current conceptualization and classification of psychiatric presentations are primarily centered on a non-biologically based clinical descriptive approach. Despite various attempts, advances in neuroscience research have not led to an improved conceptualization or mechanistic classification of psychiatric disorders. This perspective article proposes a new-work-in-progress-framework for conceptualizing psychiatric presentations based on neural network components (NNC). This framework could guide the development of mechanistic disease classification, improve understanding of underpinning pathology, and provide specific intervention targets. This model also has the potential to dissolve artificial barriers between the fields of psychiatry and neurology.

Clinical recommendations for non-invasive ultrasound neuromodulation

Non-invasive ultrasound neuromodulation has experienced exponential growth in the neuroscientific literature, recently also including clinical studies and applications. However, clinical recommendations for the secure and effective application of ultrasound neuromodulation in pathological brains are currently lacking. Here, clinical experts with neuroscientific expertise in clinical brain stimulation and ultrasound neuromodulation present initial clinical recommendations for ultrasound neuromodulation with relevance for all ultrasound neuromodulation techniques. The recommendations start with methodological safety issues focusing on technical issues to avoid harm to the brain. This is followed by clinical safety issues focusing on important factors concerning pathological situations.

Chondroitin sulfate proteoglycans mRNA expression and degradation in the zebra finch following traumatic brain injury

Traumatic brain injury (TBI) is one of the leading causes of fatality and disability worldwide. From minutes to months following damage, injury can result in a complex pathophysiology that can lead to temporary or permanent deficits including an array of neurodegenerative symptoms. These changes can include behavioral dysregulation, memory dysfunctions, and mood changes including depression. The nature and severity of impairments resulting from TBIs vary widely given the range of injury type, location, and extent of brain tissue involved. In response to the injury, the brain induces structural and functional changes to promote repair and minimize injury size. Despite its high prevalence, effective treatment strategies for TBI are limited. PNNs are part of the neuronal extracellular matrix (ECM) that mediate synaptic stabilization in the adult brain and thus neuroplasticity. They are associated mostly with inhibitory GABAergic interneurons and are thought to be responsible for maintaining the excitatory/inhibitory balance of the brain. The major structural components of PNNs include multiple chondroitin sulfate proteoglycans (CSPGs) as well as other structural proteins. Here we examine the effects of injury on CSPG expression, specifically around the changes in the side change moieties. To investigate CSPG expression following injury, adult male and female zebra finches received either a bilateral penetrating, or no injury and qPCR analysis and immunohistochemistry for components of the CSPGs were examined at 1- or 7-days post-injury. Next, to determine if CSPGs and thus PNNs should be a target for therapeutic intervention, CSPG side chains were degraded at the time of injury with chondroitinase ABC (ChABC) CSPGs moieties were examined. Additionally, GABA receptor mRNA and aromatase mRNA expression was quantified following CSPG degradation as they have been implicated in neuronal survival and neurogenesis. Our data indicate the CSPG moieties change following injury, potentially allowing for a brief period of synaptic reorganization, and that treatments that target CSPG side chains are successful in further targeting this brief critical period by decreasing GABA mRNA receptor expression, but also decreasing aromatase expression.

Divergent suicidal symptomatic activations converge on somato-cognitive action network in depression

Individuals with depression have the highest lifetime prevalence of suicide attempts (SA) among mental illnesses. Numerous neuroimaging studies have developed biomarkers from task-related neural activation in depressive patients with SA, but the findings are inconsistent. Empowered by the contemporary interconnected view of depression as a neural system disorder, we sought to identify a specific brain circuit utilizing published heterogeneous neural activations. We systematically reviewed all published cognitive and emotional task-related functional MRI studies that investigated differences in the location of neural activations between depressive patients with and without SA. We subsequently mapped an underlying brain circuit functionally connecting to each experimental activation using a large normative connectome database (n = 1000). The identified SA-related functional network was compared to the network derived from the disease control group. Finally, we decoded this convergent functional connectivity network using microscale transcriptomic and chemo-architectures, and macroscale psychological processes. We enrolled 11 experimental tasks from eight studies, including depressive patients with SA (n = 147) and without SA (n = 196). The heterogeneous SA-related neural activations localized to the somato-cognitive action network (SCAN), exhibiting robustness to little perturbations and specificity for depression. Furthermore, the SA-related functional network was colocalized with brain-wide gene expression involved in inflammatory and immunity-related biological processes and aligned with the distribution of the GABA and noradrenaline neurotransmitter systems. The findings demonstrate that the SA-related functional network of depression is predominantly located at the SCAN, which is an essential implication for understanding depressive patients with SA.

Review of the Brain's Behaviour after Injury and Disease for Its Application in an Agent-Based Model (ABM)

The brain is the most complex organ in the human body and, as such, its study entails great challenges (methodological, theoretical, etc.). Nonetheless, there is a remarkable amount of studies about the consequences of pathological conditions on its development and functioning. This bibliographic review aims to cover mostly findings related to changes in the physical distribution of neurons and their connections-the connectome-both structural and functional, as well as their modelling approaches. It does not intend to offer an extensive description of all conditions affecting the brain; rather, it presents the most common ones. Thus, here, we highlight the need for accurate brain modelling that can subsequently be used to understand brain function and be applied to diagnose, track, and simulate treatments for the most prevalent pathologies affecting the brain.

Brain lesions causing parkinsonism versus seizures map to opposite brain networks

Recent epidemiological studies propose an association between parkinsonism and seizures, but the direction of this association is unclear. Focal brain lesions causing new-onset parkinsonism versus seizures may provide a unique perspective on the causal relationship between the two symptoms and involved brain networks. We studied lesions causing parkinsonism versus lesions causing seizures and used the human connectome to identify their connected brain networks. Brain networks for parkinsonism and seizures were compared using spatial correlations on a group and individual lesion level. Lesions not associated with either symptom were used as controls. Lesion locations from 29 patients with parkinsonism were connected to a brain network with the opposite spatial topography (spatial r = -0.85) compared to 347 patients with lesions causing seizures. A similar inverse relationship was found when comparing the connections that were most specific on a group level (spatial r = -0.51) and on an individual lesion level (average spatial r = -0.042; P < 0.001). The substantia nigra was found to be most positively correlated to the parkinsonism network but most negatively correlated to the seizure network (spatial r > 0.8). Brain lesions causing parkinsonism versus seizures map to opposite brain networks, providing neuroanatomical insight into conflicting epidemiological evidence.

Effect of Different Staining Methods on Brain Cryosections

Staining frozen sections is often required to distinguish cell types for spatial transcriptomic studies of the brain. The impact of the staining methods on the RNA integrity of the cells becomes one of the limitations of spatial transcriptome technology with microdissection. However, there is a lack of systematic comparisons of different staining modalities for the pretreatment of frozen sections of brain tissue as well as their effects on transcriptome sequencing results. In this study, four different staining methods were analyzed for their effect on RNA integrity in frozen sections of brain tissue. Subsequently, differences in RNA quality in frozen sections under different staining conditions and their impact on transcriptome sequencing results were assessed by RNA-seq. As one of the most commonly used methods for staining pathological sections, HE staining seriously affects the RNA quality of frozen sections of brain tissue. In contrast, the homemade cresyl violet staining method developed in this study has the advantages of short staining time, low cost, and less RNA degradation. The homemade cresyl violet staining proposed in this study can be applied instead of HE staining as an advance staining step for transcriptome studies in frozen sections of brain tissue. In the future, this staining method may be suitable for wide application in brain-related studies of frozen tissue sections. Moreover, it is expected to become a routine step for staining cells before sampling in brain science.

Localization of stuttering based on causal brain lesions

Stuttering affects approximately 1 in 100 adults and can result in significant communication problems and social anxiety. It most often occurs as a developmental disorder but can also be caused by focal brain damage. These latter cases may lend unique insight into the brain regions causing stuttering. Here, we investigated the neuroanatomical substrate of stuttering using three independent datasets: (i) case reports from the published literature of acquired neurogenic stuttering following stroke (n = 20, 14 males/six females, 16-77 years); (ii) a clinical single study cohort with acquired neurogenic stuttering following stroke (n = 20, 13 males/seven females, 45-87 years); and (iii) adults with persistent developmental stuttering (n = 20, 14 males/six females, 18-43 years). We used the first two datasets and lesion network mapping to test whether lesions causing acquired stuttering map to a common brain network. We then used the third dataset to test whether this lesion-based network was relevant to developmental stuttering. In our literature dataset, we found that lesions causing stuttering occurred in multiple heterogeneous brain regions, but these lesion locations were all functionally connected to a common network centred around the left putamen, including the claustrum, amygdalostriatal transition area and other adjacent areas. This finding was shown to be specific for stuttering (PFWE < 0.05) and reproducible in our independent clinical cohort of patients with stroke-induced stuttering (PFWE < 0.05), resulting in a common acquired stuttering network across both stroke datasets. Within the common acquired stuttering network, we found a significant association between grey matter volume and stuttering impact for adults with persistent developmental stuttering in the left posteroventral putamen, extending into the adjacent claustrum and amygdalostriatal transition area (PFWE < 0.05). We conclude that lesions causing acquired neurogenic stuttering map to a common brain network, centred to the left putamen, claustrum and amygdalostriatal transition area. The association of this lesion-based network with symptom severity in developmental stuttering suggests a shared neuroanatomy across aetiologies.

Predictive Models for the Transition from Mild Neurocognitive Disorder to Major Neurocognitive Disorder: Insights from Clinical, Demographic, and Neuropsychological Data

Neurocognitive disorders (NCDs) are progressive conditions that severely impact cognitive function and daily living. Understanding the transition from mild to major NCD is crucial for personalized early intervention and effective management. Predictive models incorporating demographic variables, clinical data, and scores on neuropsychological and emotional tests can significantly enhance early detection and intervention strategies in primary healthcare settings. We aimed to develop and validate predictive models for the progression from mild NCD to major NCD using demographic, clinical, and neuropsychological data from 132 participants over a two-year period. Generalized Estimating Equations were employed for data analysis. Our final model achieved an accuracy of 83.7%. A higher body mass index and alcohol drinking increased the risk of progression from mild NCD to major NCD, while female sex, higher praxis abilities, and a higher score on the Geriatric Depression Scale reduced the risk. Here, we show that integrating multiple factors-ones that can be easily examined in clinical settings-into predictive models can improve early diagnosis of major NCD. This approach could facilitate timely interventions, potentially mitigating the progression of cognitive decline and improving patient outcomes in primary healthcare settings. Further research should focus on validating these models across diverse populations and exploring their implementation in various clinical contexts.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

Theory and evidence-base for a digital platform for the delivery of language tests during awake craniotomy and collaborative brain mapping

Objectives

Build the theoretical and evidence-base for a digital platform (map-OR) which delivers intraoperative language tests during awake craniotomy and facilitates collaborative sharing of brain mapping data.

Design

Mixed methodology study including two scoping reviews, international survey, synthesis of development guiding principles and a risk assessment using failure modes and effects analysis.

Setting

The two scoping reviews examined the literature published in the English language. International survey was completed by members of awake craniotomy teams from 14 countries.

Main outcome measures

Scoping review 1: number of technologies described for language mapping during awake craniotomy. Scoping review 2: barriers and facilitators to adopting novel technology in surgery. International survey: degree of language mapping technology penetration into clinical practice.

Results

A total of 12 research articles describing 6 technologies were included. The technologies required a range of hardware components including portable devices, virtual reality headsets and large integrated multiscreen stacks. The facilitators and barriers of technology adoption in surgery were extracted from 11 studies and mapped onto the 4 Unified Theory of Acceptance and Use of Technology constructs. A total of 37 awake craniotomy teams from 14 countries completed the survey. Of the responses, 20 (54.1%) delivered their language tests digitally, 10 (27.0%) delivered tests using cards and 7 (18.9%) used a combination of both. The most commonly used devices were tablet computers (67.7%; n=21) and the most common software used was Microsoft PowerPoint (60.6%; n=20). Four key risks for the proposed digital platform were identified, the highest risk being a software and internet connectivity failure during surgery.

Conclusions

This work represents a rigorous and structured approach to the development of a digital platform for standardized intraoperative language testing during awake craniotomy and for collaborative sharing of brain mapping data.

Trial registration number

Scoping review protocol registrations in OSF registries (scoping review 1: osf.io/su9xm; scoping review 2: osf.io/x4wsc).

Brain lesions causing parkinsonism versus seizures map to opposite brain networks

Recent epidemiological studies propose an association between parkinsonism and seizures, but the direction of this association is unclear. Focal brain lesions causing new-onset parkinsonism versus seizures may provide a unique perspective on the causal relationship between the two symptoms and involved brain networks. We studied lesions causing parkinsonism versus lesions causing seizures and utilized human connectome data to identify their connected brain networks. Brain networks for parkinsonism and seizures were compared using spatial correlations on a group and individual lesion level. Lesions not associated with either symptom were used as controls. Lesion locations from 29 patients with parkinsonism were connected to a brain network with the opposite spatial topography (spatial r =-0.85) compared to 347 patients with lesions causing seizures. A similar inverse relationship was found when comparing the connections that were most specific for lesions causing parkinsonism versus seizures on a group level (spatial r =- 0.51) and on an individual lesion level (average spatial r =-0.042; p<0.001). The substantia nigra was found to be most positively correlated to the parkinsonism network but most negatively correlated to the seizure network (spatial r >0.8). Brain lesions causing parkinsonism versus seizures map to opposite brain networks, providing neuroanatomical insight into conflicting epidemiological evidence.

Heterogeneous Brain Abnormalities in Schizophrenia Converge on a Common Network Associated With Symptom Remission

BACKGROUND AND HYPOTHESIS

There is a huge heterogeneity of magnetic resonance imaging findings in schizophrenia studies. Here, we hypothesized that brain regions identified by structural and functional imaging studies of schizophrenia could be reconciled in a common network.

STUDY DESIGN

We systematically reviewed the case-control studies that estimated the brain morphology or resting-state local function for schizophrenia patients in the literature. Using the healthy human connectome (n = 652) and a validated technique "coordinate network mapping" to identify a common brain network affected in schizophrenia. Then, the specificity of this schizophrenia network was examined by independent data collected from 13 meta-analyses. The clinical relevance of this schizophrenia network was tested on independent data of medication, neuromodulation, and brain lesions.

STUDY RESULTS

We identified 83 morphological and 60 functional studies comprising 7389 patients with schizophrenia and 7408 control subjects. The "coordinate network mapping" showed that the atrophy and dysfunction coordinates were functionally connected to a common network although they were spatially distant from each other. Taking all 143 studies together, we identified the schizophrenia network with hub regions in the bilateral anterior cingulate cortex, insula, temporal lobe, and subcortical structures. Based on independent data from 13 meta-analyses, we showed that these hub regions were specifically connected with regions of cortical thickness changes in schizophrenia. More importantly, this schizophrenia network was remarkably aligned with regions involving psychotic symptom remission.

CONCLUSIONS

Neuroimaging abnormalities in cross-sectional schizophrenia studies converged into a common brain network that provided testable targets for developing precise therapies.

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

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

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Causal network localization of brain stimulation targets for trait anxiety

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

The Interplay of Sports and Nutrition in Neurological Health and Recovery

This comprehensive review explores the dynamic relationship between sports, nutrition, and neurological health. Focusing on recent clinical advancements, it examines how physical activity and dietary practices influence the prevention, treatment, and rehabilitation of various neurological conditions. The review highlights the role of neuroimaging in understanding these interactions, discusses emerging technologies in neurotherapeutic interventions, and evaluates the efficacy of sports and nutritional strategies in enhancing neurological recovery. This synthesis of current knowledge aims to provide a deeper understanding of how lifestyle factors can be integrated into clinical practices to improve neurological outcomes.

The emergence of multiscale connectomics-based approaches in stroke recovery

Stroke is a leading cause of adult disability. Understanding stroke damage and recovery requires deciphering changes in complex brain networks across different spatiotemporal scales. While recent developments in brain readout technologies and progress in complex network modeling have revolutionized current understanding of the effects of stroke on brain networks at a macroscale, reorganization of smaller scale brain networks remains incompletely understood. In this review, we use a conceptual framework of graph theory to define brain networks from nano- to macroscales. Highlighting stroke-related brain connectivity studies at multiple scales, we argue that multiscale connectomics-based approaches may provide new routes to better evaluate brain structural and functional remapping after stroke and during recovery.

Discriminative functional connectivity signature of cocaine use disorder links to rTMS treatment response

Cocaine use disorder (CUD) is prevalent, and repetitive transcranial magnetic stimulation (rTMS) shows promise in reducing cravings. However, the association between a consistent CUD-specific functional connectivity signature and treatment response remains unclear. Here we identify a validated functional connectivity signature from functional magnetic resonance imaging to discriminate CUD, with successful independent replication. We found increased connectivity within the visual and dorsal attention networks and between the frontoparietal control and ventral attention networks, alongside reduced connectivity between the default mode and limbic networks in patients with CUD. These connections were associated with drug use history and cognitive impairments. Using data from a randomized clinical trial, we also established the prognostic value of these functional connectivities for rTMS treatment outcomes in CUD, especially involving the frontoparietal control and default mode networks. Our findings reveal insights into the neurobiological mechanisms of CUD and link functional connectivity biomarkers with rTMS treatment response, offering potential targets for future therapeutic development.

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

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