Target selection for deep brain stimulation in treatment resistant schizophrenia

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.

Landscape and future directions of machine learning applications in closed-loop brain stimulation

Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson's, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are "open-loop" and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of "closed-loop" systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson's, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease.

Invasive Computational Psychiatry

Computational psychiatry, a relatively new yet prolific field that aims to understand psychiatric disorders with formal theories about the brain, has seen tremendous growth in the past decade. Despite initial excitement, actual progress made by computational psychiatry seems stagnant. Meanwhile, understanding of the human brain has benefited tremendously from recent progress in intracranial neuroscience. Specifically, invasive techniques such as stereotactic electroencephalography, electrocorticography, and deep brain stimulation have provided a unique opportunity to precisely measure and causally modulate neurophysiological activity in the living human brain. In this review, we summarize progress and drawbacks in both computational psychiatry and invasive electrophysiology and propose that their combination presents a highly promising new direction-invasive computational psychiatry. The value of this approach is at least twofold. First, it advances our mechanistic understanding of the neural computations of mental states by providing a spatiotemporally precise depiction of neural activity that is traditionally unattainable using noninvasive techniques with human subjects. Second, it offers a direct and immediate way to modulate brain states through stimulation of algorithmically defined neural regions and circuits (i.e., algorithmic targeting), thus providing both causal and therapeutic insights. We then present depression as a use case where the combination of computational and invasive approaches has already shown initial success. We conclude by outlining future directions as a road map for this exciting new field as well as presenting cautions about issues such as ethical concerns and generalizability of findings.

Recent advances in noninvasive brain stimulation for schizophrenia

PURPOSE OF REVIEW

Noninvasive brain stimulation has emerged in the last three decades as a promising treatment for patients with antipsychotic-resistant symptoms of schizophrenia. This review updates the latest progress in the use of noninvasive brain stimulation to treat schizophrenia symptoms.

RECENT FINDINGS

Several recently published randomized-controlled trials support a long-lasting clinical effect of stimulation techniques on schizophrenia symptoms. In addition, efforts have been made in recent months to improve efficacy through several optimization strategies. Studies have tested new parameters of stimulation, such as theta burst stimulation, and alternative cortical or subcortical targets and have reported encouraging results. New forms of electrical stimulations such as alternating and random noise stimulation, have also been studied and have shown clinical and cognitive usefulness for patients. Accelerated stimulation protocols, and prospects could arise with deeper stimulation strategies.

SUMMARY

Using brain stimulation to treat symptoms of schizophrenia seems promising and the great flexibility of the stimulation parameters leaves much room for developing optimization strategies and improving its effectiveness. Further studies need to identify the optimal parameters to maximize response rate.

Toward recovery in schizophrenia: Current concepts, findings, and future research directions

Schizophrenia was initially defined as "dementia praecox" by E. Kraepelin, which implies progressive deterioration. However, recent studies have revealed that early effective intervention may lead to social and functional recovery in schizophrenia. In this review, we provide an overview of current concepts in schizophrenia and pathophysiological hypotheses. In addition, we present recent findings from clinical and basic research on schizophrenia. Recent neuroimaging and neurophysiological studies have consistently revealed specific biological differences in the structure and function of the brain in those with schizophrenia. From a basic research perspective, to determine the essential pathophysiology underlying schizophrenia, it is crucial that findings from all lines of inquiry-induced pluripotent stem cell (iPSC)-derived neural cells from patients, murine models expressing genetic mutations identified in patients, and patient clinical data-be integrated to contextualize the analysis results. However, the findings remain insufficient to serve as a diagnostic tool or a biomarker for predicting schizophrenia-related outcomes. Collaborations to conduct clinical research based on the patients' and their families' values are just beginning, and further development is expected.

Diving into the Subcortex: The Potential of Chronic Subcortical Sensing for Unravelling Basal Ganglia Function and Optimization of Deep Brain STIMULATION

Subcortical structures are a relative neurophysiological 'terra incognita' owing to their location within the skull. While perioperative subcortical sensing has been performed for more than 20 years, the neurophysiology of the basal ganglia in the home setting has remained almost unexplored. However, with the recent advent of implantable pulse generators (IPG) that are able to record neural activity, the opportunity to chronically record local field potentials (LFPs) directly from electrodes implanted for deep brain stimulation opens up. This allows for a breakthrough of chronic subcortical sensing into fundamental research and clinical practice. In this review an extensive overview of the current state of subcortical sensing is provided. The widespread potential of chronic subcortical sensing for investigational and clinical use is discussed. Finally, status and future perspectives of the most promising application of chronic subcortical sensing -i.e., adaptive deep brain stimulation (aDBS)- are discussed in the context of movement disorders. The development of aDBS based on both chronic subcortical and cortical sensing has the potential to dramatically change clinical practice and the life of patients with movement disorders. However, several barriers still stand in the way of clinical implementation. Advancements regarding IPG and lead technology, physiomarkers, and aDBS algorithms as well as harnessing artificial intelligence, multimodality and sensing in the naturalistic setting are needed to bring aDBS to clinical practice.

Developmental Stressors Induce Innate Immune Memory in Microglia and Contribute to Disease Risk

Many types of stressors have an impact on brain development, function, and disease susceptibility including immune stressors, psychosocial stressors, and exposure to drugs of abuse. We propose that these diverse developmental stressors may utilize a common mechanism that underlies impaired cognitive function and neurodevelopmental disorders such as schizophrenia, autism, and mood disorders that can develop in later life as a result of developmental stressors. While these stressors are directed at critical developmental windows, their impacts are long-lasting. Immune activation is a shared pathophysiology across several different developmental stressors and may thus be a targetable treatment to mitigate the later behavioral deficits. In this review, we explore different types of prenatal and perinatal stressors and their contribution to disease risk and underlying molecular mechanisms. We highlight the impact of developmental stressors on microglia biology because of their early infiltration into the brain, their critical role in brain development and function, and their long-lived status in the brain throughout life. Furthermore, we introduce innate immune memory as a potential underlying mechanism for developmental stressors' impact on disease. Finally, we highlight the molecular and epigenetic reprogramming that is known to underlie innate immune memory and explain how similar molecular mechanisms may be at work for cells to retain a long-term perturbation after exposure to developmental stressors.