The rostral zona incerta: a subcortical integrative hub and potential DBS target for OCD

Nuclei-specific hypothalamus networks predict a dimensional marker of stress in humans

The hypothalamus is part of the hypothalamic-pituitary-adrenal axis which activates stress responses through release of cortisol. It is a small but heterogeneous structure comprising multiple nuclei. In vivo human neuroimaging has rarely succeeded in recording signals from individual hypothalamus nuclei. Here we use human resting-state fMRI (n = 498) with high spatial resolution to examine relationships between the functional connectivity of specific hypothalamic nuclei and a dimensional marker of prolonged stress. First, we demonstrate that we can parcellate the human hypothalamus into seven nuclei in vivo. Using the functional connectivity between these nuclei and other subcortical structures including the amygdala, we significantly predict stress scores out-of-sample. Predictions use 0.0015% of all possible brain edges, are specific to stress, and improve when using nucleus-specific compared to whole-hypothalamus connectivity. Thus, stress relates to connectivity changes in precise and functionally meaningful subcortical networks, which may be exploited in future studies using interventions in stress disorders.

Coupled Activation of the Hyperdirect and Cerebellothalamic Pathways with Zona Incerta Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) or ventral intermediate nucleus (VIM) are established targets for the treatment of Parkinson's disease (PD) or essential tremor (ET), respectively. However, DBS of the zona incerta (ZI) can be effective for both disorders. VIM DBS is assumed to achieve its therapeutic effect via activation of the cerebellothalamic (CBT) pathway, whereas the activation of the hyperdirect (HD) pathway likely plays a role in the mechanisms of STN DBS. Interestingly, HD pathway axons also emit collaterals to the ZI and red nucleus (RN) and the CBT pathway courses nearby to the ZI.

OBJECTIVE

The aim was to examine the ability of ZI DBS to mutually activate the HD and CBT pathways in a detailed computational model of human DBS.

METHODS

We extended a previous model of the human HD pathway to incorporate axon collaterals to the ZI and RN. The anatomical framework of the model system also included representations of the CBT pathway and internal capsule (IC) fibers of passage. We then performed detailed biophysical simulations to quantify DBS activation of the HD, CBT, and IC pathways with electrodes located in either the STN or ZI.

RESULTS

STN DBS and ZI DBS both robustly activated the HD pathway. However, STN DBS was limited by IC activation at higher stimulus amplitudes. Alternatively, ZI DBS avoided IC activation while simultaneously activating the HD and CBT pathways.

CONCLUSIONS

From both neuroanatomical and biophysical perspectives, ZI DBS represents an advantageous target for coupled activation of the HD and CBT pathways. © 2024 International Parkinson and Movement Disorder Society.

Curiosity: primate neural circuits for novelty and information seeking

For many years, neuroscientists have investigated the behavioural, computational and neurobiological mechanisms that support value-based decisions, revealing how humans and animals make choices to obtain rewards. However, many decisions are influenced by factors other than the value of physical rewards or second-order reinforcers (such as money). For instance, animals (including humans) frequently explore novel objects that have no intrinsic value solely because they are novel and they exhibit the desire to gain information to reduce their uncertainties about the future, even if this information cannot lead to reward or assist them in accomplishing upcoming tasks. In this Review, I discuss how circuits in the primate brain responsible for detecting, predicting and assessing novelty and uncertainty regulate behaviour and give rise to these behavioural components of curiosity. I also briefly discuss how curiosity-related behaviours arise during postnatal development and point out some important reasons for the persistence of curiosity across generations.

Diffusion Magnetic Resonance Imaging Tractography Guides Investigation of the Zona Incerta: A Novel Target for Deep Brain Stimulation

BACKGROUND

The zona incerta (ZI) is a subcortical structure primarily investigated in rodents that is implicated in various behaviors, ranging from motor control to survival-associated activities, partly due to its integration in multiple neural circuits. In the current study, we used diffusion magnetic resonance imaging tractography to segment the ZI and gain insight into its connectivity in various circuits in humans.

METHODS

We performed probabilistic tractography in 7T diffusion MRI on 178 participants from the Human Connectome Project to validate the ZI's anatomical subdivisions and their respective tracts. K-means clustering segmented the ZI based on each voxel's connectivity profile. We further characterized the connections of each ZI subregion using probabilistic tractography with each subregion as a seed.

RESULTS

We identified 2 dominant clusters that delineated the whole ZI into rostral and caudal subregions. The caudal ZI primarily connected with motor regions, while the rostral ZI received a topographic distribution of projections from prefrontal areas, notably the anterior cingulate and medial prefrontal cortices. We generated a probabilistic ZI atlas that was registered to a patient-participant's magnetic resonance imaging scan for placement of stereoencephalographic leads for electrophysiology-guided deep brain stimulation to treat their obsessive-compulsive disorder. Rostral ZI stimulation improved the patient's core symptoms (mean improvement 21%).

CONCLUSIONS

We present a tractography-based atlas of the rostral and caudal ZI subregions constructed using high-resolution diffusion magnetic resonance imaging from 178 healthy participants. Our work provides an anatomical foundation to explore the rostral ZI as a novel target for deep brain stimulation to treat refractory obsessive-compulsive disorder and other disorders associated with dysfunctional reward circuitry.

Input-output relation of midbrain connectomics in a rodent model of depression

BACKGROUND

The symptoms associated with depression are believed to arise from disruptions in information processing across brain networks. The ventral tegmental area (VTA) influences reward-based behavior, motivation, addiction, and psychiatric disorders, including depression. Deep brain stimulation (DBS) of the medial forebrain bundle (MFB), is an emerging therapy for treatment-resistant depression. Understanding the depression associated anatomical networks crucial for comprehending its antidepressant effects.

METHODS

Flinders Sensitive Line (FSL), a rodent model of depression and Sprague-Dawley rats (n = 10 each) were used in this study. We used monosynaptic tracing to map inputs of VTA efferent neurons: VTA-to-NAc nucleus accumbens (NAc) (both core and shell) and VTA-to-prefrontal cortex (PFC). Quantitative analysis explored afferent diversity and strengths.

RESULTS

VTA efferent neurons receive a variety of afferents with varying input weights and predominant neuromodulatory representation. Notably, NAc-core projecting VTA neurons showed stronger afferents from dorsal raphe, while NAc shell-projecting VTA neurons displayed lower input strengths from cortex, thalamus, zona incerta and pretectal area in FSL rats. NAc shell-projecting VTA neurons showed the most difference in connectivity across the experimental groups.

LIMITATIONS

Lack of functional properties of the anatomical connections is the major limitation of this study. Incomplete labeling and the cytotoxicity of the rabies virus should be made aware of.

CONCLUSIONS

These findings provide the first characterization of inputs to different VTA ascending projection neurons, shedding light on critical differences in the connectome of the midbrain-forebrain system. Moreover, these differences support potential network effects of these circuits in the context of MFB DBS neuromodulation for depression.

Disentangling the identity of the zona incerta: a review of the known connections and latest implications

The zona incerta (ZI) is a subthalamic region composed by loosely packed neurochemically mixed neurons, juxtaposed to the main ascending and descending bundles. The extreme neurochemical diversity that characterizes this area, together with the diffuseness of its connections with the entire neuraxis and its hard-to-reach positioning in the brain caused the ZI to keep its halo of mystery for over a century. However, in the last decades, a rich albeit fragmentary body of knowledge regarding both the incertal anatomical connections and functional implications has been built mostly based on rodent studies and its lack of cohesion makes difficult to depict an integrated, exhaustive picture regarding the ZI and its roles. This review aims to provide a unified resource that summarizes the current knowledge regarding the anatomical profile of interactions of the ZI in rodents and non-human primates and the functional significance of its connections, highlighting the aspects still unbeknown to research.

Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deeplearning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing.