Brain Circuits Research Articles

The future of transcranial ultrasound as a precision brain interface

Our understanding of brain circuit operations and disorders has rapidly outpaced our ability to intervene and restore them. Developing technologies that can precisely interface with any brain region and circuit may combine diagnostics with therapeutic intervention, expediting personalised brain medicine. Transcranial ultrasound stimulation (TUS) is a promising noninvasive solution to this challenge, offering focal precision and scalability. By exploiting the biomechanics of pressure waves on brain tissue, TUS enables multi-site targeted neuromodulation across distributed circuits in the cortex and deeper areas alike. In this Essay, we explore the emergent evidence that TUS can functionally test and modify dysfunctional regions, effectively serving as a search and rescue tool for the brain. We define the challenges and opportunities faced by TUS as it moves towards greater target precision and integration with advanced brain monitoring and interventional technology. Finally, we propose a roadmap for the evolution of TUS as it progresses from a research tool to a clinically validated therapeutic for brain disorders.

Reprogramming astroglia into neurons with hallmarks of fast-spiking parvalbumin-positive interneurons by phospho-site-deficient Ascl1.

Cellular reprogramming of mammalian glia to an induced neuronal fate holds the potential for restoring diseased brain circuits. While the proneural factor achaete-scute complex-like 1 (Ascl1) is widely used for neuronal reprogramming, in the early postnatal mouse cortex, Ascl1 fails to induce the glia-to-neuron conversion, instead promoting the proliferation of oligodendrocyte progenitor cells (OPC). Since Ascl1 activity is posttranslationally regulated, here, we investigated the consequences of mutating six serine phospho-acceptor sites to alanine (Ascl1SA6) on lineage reprogramming in vivo. Ascl1SA6 exhibited increased neurogenic activity in the glia of the early postnatal mouse cortex, an effect enhanced by coexpression of B cell lymphoma 2 (Bcl2). Genetic fate-mapping revealed that most induced neurons originated from astrocytes, while only a few derived from OPCs. Many Ascl1SA6/Bcl2-induced neurons expressed parvalbumin and were capable of high-frequency action potential firing. Our study demonstrates the authentic conversion of astroglia into neurons featuring subclass hallmarks of cortical interneurons, advancing our scope of engineering neuronal fates in the brain.

Alterations of regional brain activity and corresponding brain circuits in drug-naïve adolescents with nonsuicidal self-injury

Nonsuicidal self-injury (NSSI) is one of the major public health problems endangering adolescents. However, the neural mechanisms of NSSI is still unclear. The purpose of this study was to explore regional brain activity and corresponding brain circuits in drug-naïve adolescents with NSSI using amplitude of low-frequency fluctuations (ALFF) combined with functional connectivity (FC) analysis. Thirty-two drug-naïve adolescents with NSSI (NSSI group) and 29 healthy controls matched for sex, age, and level of education (HC group) were enrolled in this study. ALFF and seed-based FC analyses were used to examine the alterations in regional brain activity and corresponding brain circuits. The correlation between ALFF or FC values of aberrant brain regions and clinical characteristics were detected by Pearson correlation analysis. The NSSI group showed increased ALFF in the left inferior and middle occipital gyri, lingual gyrus, and fusiform gyrus; additionally, decreased ALFF in the right medial cingulate gyrus, left anterior cingulate gyrus, and left medial superior frontal gyrus compared to those in the HC group. With the left inferior occipital gyrus as seed, the NSSI group showed increased FC between the left inferior occipital gyrus and the bilateral superior parietal gyrus, right inferior parietal angular gyrus, right inferior frontal gyrus of the insular region, and left precuneus relative to that the HC group. With the left anterior cingulate gyrus as seed, the NSSI group showed increased FC between the left anterior cingulate gyrus and right dorsolateral superior frontal gyrus. With the left lingual gyrus as seed, the NSSI group showed increased FC between the left lingual gyrus and right middle frontal gyrus, and decreased FC between the left lingual gyrus and the left superior temporal gyrus, right supplementary motor area, and left rolandic operculum. With the left fusiform gyrus as seed, the NSSI group showed increased FC between the left fusiform gyrus and left middle and inferior temporal gyrus, and decreased FC between the left fusiform gyrus and the bilateral postcentral gyrus, right precentral gyrus, right lingual gyrus, and left inferior parietal angular gyrus. Moreover, the FC value between the left fusiform gyrus and left inferior temporal gyrus was positively correlated with suicidal ideations score. This study highlights alterations in regional brain activity and corresponding brain circuits in brain regions related to visual and emotional regulation functions in drug-naïve adolescents with NSSI. These findings may facilitate better understand the underlying neural mechanisms of NSSI in adolescents.

An overview of the pathophysiology of agitation in Alzheimer's dementia with a focus on neurotransmitters and circuits.

Alzheimer's dementia (AD) is a progressive, neurodegenerative disease often accompanied by neuropsychiatric symptoms that profoundly impact both patients and caregivers. Agitation is among the most prevalent and distressing of these symptoms and often requires treatment. Appropriate therapeutic interventions depend on understanding the biological basis of agitation and how it may be affected by treatment. This narrative review discusses a proposed pathophysiology of agitation in Alzheimer's dementia based on convergent evidence across research approaches. Available data indicate that agitation in Alzheimer's dementia is associated with an imbalance of activity between key prefrontal and subcortical brain regions. The monoamine neurotransmitter systems serve as key modulators of activity within these brain regions and circuits and are rendered abnormal in AD. Patients with AD who exhibited agitation symptoms during life have alterations in neurotransmitter nuclei and related systems when the brain is examined at autopsy. The authors present a model of agitation in Alzheimer's dementia in which noradrenergic hyperactivity along with serotonergic deficits and dysregulated striatal dopamine release contribute to agitated and aggressive behaviors.

Competitive interactions shape brain dynamics and computation across species.

Adaptive cognition relies on cooperation across anatomically distributed brain circuits. However, specialised neural systems are also in constant competition for limited processing resources. How does the brain's network architecture enable it to balance these cooperative and competitive tendencies? Here we use computational whole-brain modelling to examine the dynamical and computational relevance of cooperative and competitive interactions in the mammalian connectome. Across human, macaque, and mouse we show that the architecture of the models that most faithfully reproduce brain activity, consistently combines modular cooperative interactions with diffuse, long-range competitive interactions. The model with competitive interactions consistently outperforms the cooperative-only model, with excellent fit to both spatial and dynamical properties of the living brain, which were not explicitly optimised but rather emerge spontaneously. Competitive interactions in the effective connectivity produce greater levels of synergistic information and local-global hierarchy, and lead to superior computational capacity when used for neuromorphic computing. Altogether, this work provides a mechanistic link between network architecture, dynamical properties, and computation in the mammalian brain.

Intracranial EEG-Based Directed Functional Connectivity in Alpha to Gamma Frequency Range Reflects Local Circuits of the Human Mesiotemporal Network.

To date, it is largely unknown how frequency range of neural oscillations measured with EEG is related to functional connectivity. To address this question, we investigated frequency-dependent directed functional connectivity among the structures of mesial and anterior temporal network including amygdala, hippocampus, temporal pole and parahippocampal gyrus in the living human brain. Intracranial EEG recording was obtained from 19 consecutive epilepsy patients with normal anterior mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes. We assessed intratemporal bidirectional functional connectivity using several causality measures such as Granger causality (GC), directed transfer function (DTF) and partial directed coherence (PDC) in a frequency-specific way. In order to verify the obtained results, we compared the spontaneous functional networks with intratemporal effective connectivity evaluated by means of SPES (single pulse electrical stimulation) method. The overlap with the evoked network was found for the functional connectivity assessed by the GC method, most prominent in the higher frequency bands (alpha, beta and low gamma), yet vanishing in the lower frequencies. Functional connectivity assessed by means of DTF and PCD obtained a similar directionality pattern with the exception of connectivity between hippocampus and parahippocampal gyrus which showed opposite directionality of predominant information flow. Whereas previous connectivity studies reported significant divergence between spontaneous and evoked networks, our data show the role of frequency bands for the consistency of functional and evoked intratemporal directed connectivity. This has implications for the suitability of functional connectivity methods in characterizing local brain circuits.

Modeling and characterization of pure and odorant mixture processing in the Drosophila mushroom body calyx.

Associative memory in the Mushroom Body of the fruit fly brain depends on the encoding and processing of odorants in the first three stages of the Early Olfactory System: the Antenna, the Antennal Lobe and the Mushroom Body Calyx. The Kenyon Cells (KCs) of the Calyx provide the Mushroom Body compartments the identity of pure and odorant mixtures encoded as a train of spikes. Characterizing the code underlying the KC spike trains is a major challenge in neuroscience. To address this challenge we start by explicitly modeling the space of odorants using constructs of both semantic and syntactic information. Odorant semantics concerns the identity of odorants while odorant syntactics pertains to their concentration amplitude. These odorant attributes are multiplicatively coupled in the process of olfactory transduction. A key question that early olfactory systems must address is how to disentangle the odorant semantic information from the odorant syntactic information. To address the untanglement we devised an Odorant Encoding Machine (OEM) modeling the first three stages of early olfactory processing in the fruit fly brain. Each processing stage is modeled by Divisive Normalization Processors (DNPs). DNPs are spatio-temporal models of canonical computation of brain circuits. The end-to-end OEM is constructed as cascaded DNPs. By extensively modeling and characterizing the processing of pure and odorant mixtures in the Calyx, we seek to answer the question of its functional significance. We demonstrate that the DNP circuits in the OEM combinedly reduce the variability of the Calyx response to odorant concentration, thereby separating odorant semantic information from syntactic information. We then advance a code, called first spike sequence code, that the KCs make available at the output of the Calyx. We show that the semantics of odorants can be represented by this code in the spike domain and is ready for easy memory access in the Mushroom Body compartments.

Functional Brain Network of Trait Impulsivity: Whole-Brain Functional Connectivity Predicts Self-Reported Impulsivity.

Given impulsivity's multidimensional nature and its implications across various aspects of human behavior, a comprehensive understanding of functional brain circuits associated with this trait is warranted. In the current study, we utilized whole-brain resting-state functional connectivity data of healthy males (n = 156) to identify a network of connections predictive of an individual's impulsivity, as assessed by the Barratt Impulsiveness Scale (BIS)-11. Our participants were selected, in part, based on their self-reported BIS-11 impulsivity scores. Specifically, individuals who reported high or low trait impulsivity scores during screening were selected first, followed by those with intermediate impulsivity levels. This enabled us to include participants with rare, extreme scores and to cover the entire BIS-11 impulsivity spectrum. We employed repeated K-fold cross-validation for feature-selection and used stratified 10-fold cross-validation to train and test our models. Our findings revealed a widespread neural network associated with trait impulsivity and a notable correlation between predicted and observed scores. Feature importance and node degree were assessed to highlight specific nodes and edges within the impulsivity network, revealing previously overlooked key brain regions, such as the cerebellum, brainstem, and temporal lobe, while supporting previous findings on the basal ganglia-thalamo-prefrontal network and the prefrontal-motor strip network in relation to impulsiveness. This deepened understanding establishes a foundation for identifying alterations in functional brain networks associated with dysfunctional impulsivity.

Functional activity and connectivity signatures of ketamine and lamotrigine during negative emotional processing: a double-blind randomized controlled fMRI study

Ketamine is a highly effective antidepressant (AD) that targets the glutamatergic system and exerts profound effects on brain circuits during negative emotional processing. Interestingly, the effects of ketamine on brain measures are sensitive to modulation by pretreatment with lamotrigine, which inhibits glutamate release. Examining the antagonistic effects of ketamine and lamotrigine on glutamate transmission holds promise to identify effects of ketamine that are mediated through changes in the glutamatergic system. Investigating this modulation in relation to both the acute and sustained effects of ketamine on functional activity and connectivity during negative emotional processing should therefore provide novel insights. 75 healthy subjects were investigated in a double-blind, single-dose, randomized, placebo-controlled, parallel-group study with three treatment conditions (ketamine, lamotrigine pre-treatment, placebo). Participants completed an emotional face viewing task during ketamine infusion and 24 h later. Acute ketamine administration decreased hippocampal and Default Mode Network (DMN) activity and increased fronto-limbic coupling during negative emotional processing. Furthermore, while lamotrigine abolished the ketamine-induced increase in functional connectivity, it had no acute effect on activity. Sustained (24 h later) effects of ketamine were only found for functional activity, with a significant reduction in the posterior DMN. This effect was blocked by pretreatment with lamotrigine. Our results suggest that both the acute increases in fronto-limbic coupling and the delayed decrease in posterior DMN activity, but not the attenuated limbic and DMN recruitment after ketamine, are mediated by altered glutamatergic transmission.

Effects of oral contraceptive pills on brain networks: A replication and extension.

Neuroimaging research has identified significant effects of oral contraceptive pills (OCPs) on brain networks. A wide variety of approaches have been employed, largely in observational samples, with few converging results. This study therefore was designed to test for replication and extend this previous work using a randomized, double-blind, placebo-controlled crossover trial of the effects of OCPs on brain networks. Using functional MRI, we focused on brain regions identified in prior studies. Our analyses did not strictly replicate previously reported effects of OCPs on functional connectivity. Exploratory analyses suggested that traditional seed-based approaches may miss broader, network-level effects of OCPs on brain circuits. We applied data-driven, multivariate techniques to assess these network-level changes, A deeper understanding of neural effects of OCPs can be important in helping patients make informed decisions regarding contraception, mitigating unwanted side effects. Such information can also identify potentially confounding effects of OCPs in other neuroimaging investigations.

Microscopic deconstruction of cortical circuit stimulation by transcranial ultrasound.

Transcranial Ultrasound Stimulation (TUS) can noninvasively and reversibly perturb neuronal activity, but the mechanisms by which ultrasound engages brain circuits to induce functional effects remain unclear. To elucidate these interactions, we applied TUS to the cortex of awake mice and concurrently monitored local neural activity at the acoustic focus with two-photon calcium imaging. We show that TUS evokes highly focal responses in three canonical neuronal populations, with cell-type-specific dose dependencies. Through independent parametric variations, we demonstrate that evoked responses collectively scale with the time-average intensity of the stimulus. Finally, using computational unmixing we propose a physiologically realistic cortical circuit model that predicts TUS-evoked responses as a result of both direct effects and local network interactions. Our results provide a first direct evidence of TUS's focal effects on cortical activity and shed light on the complex circuit mechanisms underlying these effects, paving the way for TUS's deployment in clinical settings.

Modelling human neuronal catecholaminergic pigmentation in rodents recapitulates age-related neurodegenerative deficits

One key limitation in developing effective treatments for neurodegenerative diseases is the lack of models accurately mimicking the complex physiopathology of the human disease. Humans accumulate with age the pigment neuromelanin inside neurons that synthesize catecholamines. Neurons reaching the highest neuromelanin levels preferentially degenerate in Parkinson’s, Alzheimer’s and apparently healthy aging individuals. However, this brain pigment is not taken into consideration in current animal models because common laboratory species, such as rodents, do not produce neuromelanin. Here we generate a tissue-specific transgenic mouse, termed tgNM, that mimics the human age-dependent brain-wide distribution of neuromelanin within catecholaminergic regions, based on the constitutive catecholamine-specific expression of human melanin-producing enzyme tyrosinase. We show that, in parallel to progressive human-like neuromelanin pigmentation, these animals display age-related neuronal dysfunction and degeneration affecting numerous brain circuits and body tissues, linked to motor and non-motor deficits, reminiscent of early neurodegenerative stages. This model could help explore new research avenues in brain aging and neurodegeneration.

Contrastive Functional Connectivity Defines Neurophysiology-informed Symptom Dimensions in Major Depression.

Major depressive disorder (MDD) is a prevalent psychiatric disorder characterized by substantial clinical and neurobiological heterogeneity. Conventional studies that solely focus on clinical symptoms or neuroimaging metrics often fail to capture the intricate relationship between these modalities, limiting their ability to disentangle the complexity in MDD. Moreover, patient neuroimaging data typically contains normal sources of variance shared with healthy controls, which can obscure disorder-specific variance and complicate the delineation of disease heterogeneity. We employed contrastive principal component analysis to extract disorder-specific variations in fMRI-based resting-state functional connectivity (RSFC) by contrasting MDD patients (N=233) with age-matched healthy controls (N=285). We then applied sparse canonical correlation analysis to identify latent dimensions in the disorder variations by linking the extracted contrastive connectivity features to clinical symptoms in MDD patients. Two significant and generalizable dimensions linking distinct brain circuits and clinical profiles were discovered. The first dimension, associated with an apparent "internalizing-externalizing" symptom dimension, was characterized by self-connections within the visual network and also associated with choice reaction times of cognitive tasks. The second dimension, associated with personality facets such as extraversion and conscientiousness typically inversely associated with depression symptoms, is primarily driven by self-connections within the dorsal attention network. This "depression-protective personality" dimension is also associated with multiple cognitive task performances related to psychomotor slowing and cognitive control. Our contrastive RSFC-based dimensional approach offers a new avenue to dissect clinical heterogeneity underlying MDD. By identifying two stable, neurophysiology-informed symptom dimensions in MDD patients, our findings may enhance disease mechanism insights and facilitate precision phenotyping, thus advancing the development of targeted therapeutics for precision mental health.

Beyond pain privacy and pain meters: a new vision for pain biomarkers.

To an individual, pain is unambiguously real. To a caregiver, assessing pain in others is a challenging process shrouded in doubt. To explain this challenge, many assume that pain "belongs" exclusively to the bearer of that experience and accept the dogma that pain is private. However, privacy also entails that it is not possible to identify, share, or communicate that experience with others. Obviously, this is not true and the consequences of pain privacy would be devastating for healthcare. Pain is indeed unique and subjective, but not necessarily private. Pain is in fact readily communicable, though perhaps not as effectively and reliably as caregivers would like. On the other hand, healthcare systems mandate objective metrics in pain diagnosis. Smiley face caricatures are a staple of clinical practice and a universal standard for reporting pain levels. These conditions create a double paradox: Assess a private experience that is inaccessible, and use numerical scales to measure subjective attributes. Navigating this stressful environment, medical professionals experience intellectual dissonance, patients are frustrated, and value-based care is undermined. Offering a way out, first, we refute the privacy and objectification of pain citing philosophical, behavioral, and neuroscientific arguments. We discuss Wittgensteinian views against privacy, explore the clear evolutionary advantage of communicating pain to others, and identify neural circuits in the mammalian brain that contribute to empathy. Second, we highlight the subjectivity of pain, embracing the complexity and uniqueness of an individual's pain. We also provide compelling evidence for brain mechanisms that actively shape the pain experience according to predictive coding principles. Third, we offer a vision for the development of biomarker technologies that assess pain fairly without engendering bias against the patient's narrative. Our recommendations are based on the overwhelming appreciation that "medicine by emoji" is inadequate for capturing the multidimensional nature of pain. Our view is that the most promising candidates for pain biomarkers consist of self-reports as ground truth augmented by physiological signatures of biological relevance to pain. Integration of subjective and objective multimodal features will be key for the development of comprehensive pain assessment models.

Large-Scale Mechanistic Models of Brain Circuits with Biophysically and Morphologically Detailed Neurons.

Understanding the brain requires studying its multiscale interactions from molecules to networks. The increasing availability of large-scale datasets detailing brain circuit composition, connectivity, and activity is transforming neuroscience. However, integrating and interpreting this data remains challenging. Concurrently, advances in supercomputing and sophisticated modeling tools now enable the development of highly detailed, large-scale biophysical circuit models. These mechanistic multiscale models offer a method to systematically integrate experimental data, facilitating investigations into brain structure, function, and disease. This review, based on a Society for Neuroscience 2024 MiniSymposium, aims to disseminate recent advances in large-scale mechanistic modeling to the broader community. It highlights (1) examples of current models for various brain regions developed through experimental data integration; (2) their predictive capabilities regarding cellular and circuit mechanisms underlying experimental recordings (e.g., membrane voltage, spikes, local-field potential, electroencephalography/magnetoencephalography) and brain function; and (3) their use in simulating biomarkers for brain diseases like epilepsy, depression, schizophrenia, and Parkinson's, aiding in understanding their biophysical underpinnings and developing novel treatments. The review showcases state-of-the-art models covering hippocampus, somatosensory, visual, motor, auditory cortical, and thalamic circuits across species. These models predict neural activity at multiple scales and provide insights into the biophysical mechanisms underlying sensation, motor behavior, brain signals, neural coding, disease, pharmacological interventions, and neural stimulation. Collaboration with experimental neuroscientists and clinicians is essential for the development and validation of these models, particularly as datasets grow. Hence, this review aims to foster interest in detailed brain circuit models, leading to cross-disciplinary collaborations that accelerate brain research.

A Drosophila computational brain model reveals sensorimotor processing

The recent assembly of the adult Drosophila melanogaster central brain connectome, containing more than 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain1,2. Here we create a leaky integrate-and-fire computational model of the entire Drosophila brain, on the basis of neural connectivity and neurotransmitter identity3, to study circuit properties of feeding and grooming behaviours. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation4. In addition, using the model to activate neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing5—a testable hypothesis that we validate by optogenetic activation and behavioural studies. Activating different classes of gustatory neurons in the model makes accurate predictions of how several taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit, and accurately describes the circuit response upon activation of different mechanosensory subtypes6–10. Our results demonstrate that modelling brain circuits using only synapse-level connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can describe complete sensorimotor transformations.

Experience-dependent serotonergic signaling in glia regulates targeted synapse elimination.

The optimization of brain circuit connectivity based on initial environmental input occurs during critical periods characterized by sensory experience-dependent, temporally restricted, and transiently reversible synapse elimination. This precise, targeted synaptic pruning mechanism is mediated by glial phagocytosis. Serotonin signaling has prominent, foundational roles in the brain, but functions in glia, or in experience-dependent brain circuit synaptic connectivity remodeling, have been relatively unknown. Here, we discover that serotonergic signaling between glia is essential for olfactory experience-dependent synaptic glomerulus pruning restricted to a well-defined Drosophila critical period. We find that experience-dependent serotonin signaling is restricted to the critical period, with both (1) serotonin production and (2) 5-HT2A receptors specifically in glia, but not neurons, absolutely required for targeted synaptic glomerulus pruning. We discover that glial 5-HT2A receptor signaling limits the experience-dependent synaptic connectivity pruning in the critical period and that conditional reexpression of 5-HT2A receptors within adult glia reestablishes "critical period-like" experience-dependent synaptic glomerulus pruning at maturity. These results reveal an essential requirement for glial serotonergic signaling mediated by 5-HT2A receptors for experience-dependent synapse elimination.

Substance P and dopamine form a "push-pull" system that diurnally regulates retinal gain.

The operation of the retina, like other brain circuits, is under modulatory control. One coordinator of changes in retinal function is dopamine, a neuromodulator released in a light-dependent way to adjust vision on a diurnal cycle. Here, we demonstrate that substance P is a similarly powerful retinal modulator that interacts with the dopamine system. By imaging glutamatergic synaptic transmission in larval zebrafish, we find that substance P decreases the contrast sensitivity of ON and OFF visual channels up to 8-fold, with suppression of visual signals being strongest through the "transient" pathway responding to higher frequencies. These actions are exerted in the morning, in large part by suppressing the amplification of visual signals by dopamine, but substance P is almost completely inactive in the afternoon. Modulation of retinal gain is accompanied by changes in patterns of vesicle release at the synapses of bipolar cells: increased gain shifts coding of stimulus strength from the rate of release events to their amplitude generated by a process of multivesicular release (MVR). Together, these actions of substance P reduce the flow of visual information, measured in bits, ∼3-fold. Thus, whereas dopamine "pushes" the retina to transmit information at higher rates in the afternoon, substance P acts in antiphase to suppress dopamine signaling and "pull down" information transmission in the morning.

DIETARY REGULATION OF SILENT SYNAPSES IN THE DORSOLATERAL STRIATUM.

Obesity and drugs of abuse share overlapping neural circuits and behaviors. Silent synapses are transient synapses that are important for remodeling brain circuits. They are prevalent during early development but largely disappear by adulthood. Drugs of abuse increase silent synapses during adulthood and may facilitate reorganizing brain circuits around drug-related experience, facilitating addiction and contributing to relapse during treatment and abstinence. Whether obesity causes alterations in the expression of silent synapses in a manner similar to drugs of abuse has not been examined. Using a dietary-induced obesity paradigm, mice that chronically consumed high fat diet (HFD) exhibited increased silent synapses in both direct and indirect pathway medium spiny neurons in the dorsolateral striatum. Both the time of onset of increased silent synapses and their normalization upon discontinuation of HFD occurs on an extended time scale compared to drugs of abuse. These data demonstrate that chronic consumption of HFD, like drugs of abuse, can alter mechanisms of circuit plasticity likely facilitating neural reorganization analogous to drugs of abuse.

Mapping lesion-related human aggression to a common brain network

BackgroundAggression 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. MethodsUsing 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. ResultsWe 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. ConclusionsWe 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.