Robust thalamic nuclei segmentation method based on local diffusion magnetic resonance properties

A multimodal characterization of low-dimensional thalamocortical structural connectivity patterns

The human thalamus is a heterogeneous subcortical structure coordinating whole-brain activity. Investigations of its internal organization reveal differentiable subnuclei, however, a consensus on subnuclei boundaries remains absent. Recent work suggests that thalamic organization additionally reflects continuous axes transcending nuclear boundaries. Here, we study how low-dimensional axes of thalamocortical structural connectivity relate to intrathalamic microstructural features, functional connectivity, and structural covariance. Using diffusion MRI, we compute a thalamocortical structural connectome and derive two main axes of thalamic organization. The principal axis, extending from medial to lateral, relates to intrathalamic myelin, and functional connectivity organization. The secondary axis corresponds to the core-matrix cell distribution. Lastly, exploring multimodal associations globally, we observe the principal axis consistently differentiating limbic, frontoparietal, and default mode network nodes from dorsal and ventral attention networks across modalities. However, the link with sensory modalities varies. In sum, we show the coherence between lower dimensional patterns of thalamocortical structural connectivity and various modalities, shedding light on multiscale thalamic organization.

Revolutionizing treatment for disorders of consciousness: a multidisciplinary review of advancements in deep brain stimulation

Among the existing research on the treatment of disorders of consciousness (DOC), deep brain stimulation (DBS) offers a highly promising therapeutic approach. This comprehensive review documents the historical development of DBS and its role in the treatment of DOC, tracing its progression from an experimental therapy to a detailed modulation approach based on the mesocircuit model hypothesis. The mesocircuit model hypothesis suggests that DOC arises from disruptions in a critical network of brain regions, providing a framework for refining DBS targets. We also discuss the multimodal approaches for assessing patients with DOC, encompassing clinical behavioral scales, electrophysiological assessment, and neuroimaging techniques methods. During the evolution of DOC therapy, the segmentation of central nuclei, the recording of single-neurons, and the analysis of local field potentials have emerged as favorable technical factors that enhance the efficacy of DBS treatment. Advances in computational models have also facilitated a deeper exploration of the neural dynamics associated with DOC, linking neuron-level dynamics with macroscopic behavioral changes. Despite showing promising outcomes, challenges remain in patient selection, precise target localization, and the determination of optimal stimulation parameters. Future research should focus on conducting large-scale controlled studies to delve into the pathophysiological mechanisms of DOC. It is imperative to further elucidate the precise modulatory effects of DBS on thalamo-cortical and cortico-cortical functional connectivity networks. Ultimately, by optimizing neuromodulation strategies, we aim to substantially enhance therapeutic outcomes and greatly expedite the process of consciousness recovery in patients.

A roadmap towards standardized neuroimaging approaches for human thalamic nuclei

The thalamus has a key role in mediating cortical-subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.

Altered thalamocortical tract trajectory growth with undisrupted thalamic parcellation pattern in human lissencephaly brain at mid-gestational stage

Proper topographically organized neural connections between the thalamus and the cerebral cortex are mandatory for thalamus function. Thalamocortical (TC) fiber growth begins during the embryonic period and completes by the third trimester of gestation, so that human neonates at birth have a thalamus with a near-facsimile of adult functional parcellation. Whether congenital neocortical anomaly (e.g., lissencephaly) affects TC connection in humans is unknown. Here, via diffusion MRI fiber-tractography analysis of long-term formalin-fixed postmortem fetal brain diagnosed as lissencephaly in comparison with an age-matched normal one, we found similar topological patterns of thalamic subregions and of internal capsule parcellated by TC fibers. However, lissencephaly fetal brain showed white matter structural changes, including fewer/less organized TC fibers and optic radiations, and much less cortical plate invasion by TC fibers - particularly around the shallow central sulcus. Diffusion MRI fiber tractography of normal fetal brains at 15, 23, and 26 gestational weeks (GW) revealed dynamic volumetric change of each parcellated thalamic subregion, suggesting coupled developmental progress of the thalamus with the corresponding cortex. Moreover, from GW23 and GW26 normal fetal brains, TC endings in the cortical plate could be delineated to reflect cumulative progressive TC invasion of cortical plate. By contrast, lissencephaly brain showed a dramatic decrease in TC invasion of the cortical plate. Our study thus shows the feasibility of diffusion MRI fiber tractography in postmortem long-term formalin-fixed fetal brains to disclose the developmental progress of TC tracts coordinating with thalamic and neocortical growth both in normal and lissencephaly fetal brains at mid-gestational stage.

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for the use of T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3 T and 7 T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

Evolution of the Transforaminal Lumbar Interbody Fusion (TLIF): From Open to Percutaneous to Patient-Specific

The transforaminal lumbar interbody fusion (TLIF) has seen significant evolution since its early inception, reflecting advancements in surgical techniques, patient safety, and outcomes. Originally described as an improvement over the posterior lumbar interbody fusion (PLIF), the TLIF began as an open surgical procedure, that notably reduced the need for the extensive neural retractation that hindered the PLIF. In line with the broader practice of surgery, trending toward minimally invasive access, the TLIF was followed by the development of the minimally invasive TLIF (MIS-TLIF), a technique that further decreased tissue trauma and postoperative complications. Subsequent advancements, including Trans-Kambin's Triangle TLIF (percLIF) and transfacet LIF, have continued to refine surgical access, minimize surgical footprint, and reduce the risk of injury to the patient. The latest evolution, as we will describe it, the patient-specific TLIF, is a culmination of the aforementioned adaptations and incorporates advanced imaging and segmentation technologies into perioperative planning, allowing surgeons to tailor approaches based on individual patient anatomy and pathology. These developments signify a shift towards more precise methods in spine surgery. The ongoing evolution of the TLIF technique illustrates the dynamic nature of surgery and emphasizes the need for continued adaptation and refinement.

Prior-guided individualized thalamic parcellation based on local diffusion characteristics

Comprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior-guided parcellation (PG-par) method to achieve robust individualized thalamic parcellation based on a central-boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high-quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high-probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test-retest assessment, the proposed prior-guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG-par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior-guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior-guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.

DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei

The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.

Subcortical contributions to the sense of body ownership

The sense of body ownership (i.e. the feeling that our body or its parts belong to us) plays a key role in bodily self-consciousness and is believed to stem from multisensory integration. Experimental paradigms such as the rubber hand illusion have been developed to allow the controlled manipulation of body ownership in laboratory settings, providing effective tools for investigating malleability in the sense of body ownership and the boundaries that distinguish self from other. Neuroimaging studies of body ownership converge on the involvement of several cortical regions, including the premotor cortex and posterior parietal cortex. However, relatively less attention has been paid to subcortical structures that may also contribute to body ownership perception, such as the cerebellum and putamen. Here, on the basis of neuroimaging and neuropsychological observations, we provide an overview of relevant subcortical regions and consider their potential role in generating and maintaining a sense of ownership over the body. We also suggest novel avenues for future research targeting the role of subcortical regions in making sense of the body as our own.

Robust thalamic nuclei segmentation from T1-weighted MRI using polynomial intensity transformation

Accurate segmentation of thalamic nuclei, crucial for understanding their role in healthy cognition and in pathologies, is challenging to achieve on standard T1-weighted (T1w) magnetic resonance imaging (MRI) due to poor image contrast. White-matter-nulled (WMn) MRI sequences improve intrathalamic contrast but are not part of clinical protocols or extant databases. In this study, we introduce histogram-based polynomial synthesis (HIPS), a fast preprocessing transform step that synthesizes WMn-like image contrast from standard T1w MRI using a polynomial approximation for intensity transformation. HIPS was incorporated into THalamus Optimized Multi-Atlas Segmentation (THOMAS) pipeline, a method developed and optimized for WMn MRI. HIPS-THOMAS was compared to a convolutional neural network (CNN)-based segmentation method and THOMAS modified for T1w images (T1w-THOMAS). The robustness and accuracy of the three methods were tested across different image contrasts (MPRAGE, SPGR, and MP2RAGE), scanner manufacturers (PHILIPS, GE, and Siemens), and field strengths (3T and 7T). HIPS-transformed images improved intra-thalamic contrast and thalamic boundaries, and HIPS-THOMAS yielded significantly higher mean Dice coefficients and reduced volume errors compared to both the CNN method and T1w-THOMAS. Finally, all three methods were compared using the frequently travelling human phantom MRI dataset for inter- and intra-scanner variability, with HIPS displaying the least inter-scanner variability and performing comparably with T1w-THOMAS for intra-scanner variability. In conclusion, our findings highlight the efficacy and robustness of HIPS in enhancing thalamic nuclei segmentation from standard T1w MRI.

Thalamic Segmentation and Neural Activation Modeling Based on Individual Tissue Microstructure in Deep Brain Stimulation for Essential Tremor

OBJECTIVE

Thalamic deep brain stimulation (DBS) is the primary surgical therapy for essential tremor (ET). Thalamic DBS traditionally uses an atlas-based targeting approach, which, although nominally accurate, may obscure individual anatomic differences from population norms. The objective of this study was to compare this traditional atlas-based approach with a novel quantitative modeling methodology grounded in individual tissue microstructure (N-of-1 approach).

MATERIALS AND METHODS

The N-of-1 approach uses individual patient diffusion tensor imaging (DTI) data to perform thalamic segmentation and volume of tissue activation (VTA) modeling. For each patient, the thalamus was individually segmented into 13 nuclei using DTI-based k-means clustering. DBS-induced VTAs associated with tremor suppression and side effects were then computed for each patient with finite-element electric-field models incorporating DTI microstructural data. Results from N-of-1 and traditional atlas-based modeling were compared for a large cohort of patients with ET treated with thalamic DBS.

RESULTS

The size and shape of individual N-of-1 thalamic nuclei and VTAs varied considerably across patients (N = 22). For both methods, tremor-improving therapeutic VTAs showed similar overlap with motor thalamic nuclei and greater motor than sensory nucleus overlap. For VTAs producing undesirable sustained paresthesia, 94% of VTAs overlapped with N-of-1 sensory thalamus estimates, whereas 74% of atlas-based segmentations overlapped. For VTAs producing dysarthria/motor contraction, the N-of-1 approach predicted greater spread beyond the thalamus into the internal capsule and adjacent structures than the atlas-based method.

CONCLUSIONS

Thalamic segmentation and VTA modeling based on individual tissue microstructure explain therapeutic stimulation equally well and side effects better than a traditional atlas-based method in DBS for ET. The N-of-1 approach may be useful in DBS targeting and programming, particularly when patient neuroanatomy deviates from population norms.

Characterizing functional modules in the human thalamus: coactivation-based parcellation and systems-level functional decoding

The human thalamus relays sensory signals to the cortex and facilitates brain-wide communication. The thalamus is also more directly involved in sensorimotor and various cognitive functions but a full characterization of its functional repertoire, particularly in regard to its internal anatomical structure, is still outstanding. As a putative hub in the human connectome, the thalamus might reveal its functional profile only in conjunction with interconnected brain areas. We therefore developed a novel systems-level Bayesian reverse inference decoding that complements the traditional neuroinformatics approach towards a network account of thalamic function. The systems-level decoding considers the functional repertoire (i.e., the terms associated with a brain region) of all regions showing co-activations with a predefined seed region in a brain-wide fashion. Here, we used task-constrained meta-analytic connectivity-based parcellation (MACM-CBP) to identify thalamic subregions as seed regions and applied the systems-level decoding to these subregions in conjunction with functionally connected cortical regions. Our results confirm thalamic structure-function relationships known from animal and clinical studies and revealed further associations with language, memory, and locomotion that have not been detailed in the cognitive neuroscience literature before. The systems-level decoding further uncovered large systems engaged in autobiographical memory and nociception. We propose this novel decoding approach as a useful tool to detect previously unknown structure-function relationships at the brain network level, and to build viable starting points for future studies.

Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas

The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).

Serial deep gray nuclear DTI changes in Parkinson's disease over twelve years

Background

Deep gray nuclear pathology relates to motor deterioration in idiopathic Parkinson's disease (PD). Inconsistent deep nuclear diffusion tensor imaging (DTI) findings in cross-sectional or short-term longitudinal studies have been reported. Long-term studies in PD are clinically challenging; decade-long deep nuclear DTI data are nonexistent. We investigated serial DTI changes and clinical utility in a case-control PD cohort of 149 subjects (72 patients/77 controls) over 12 years.

Methods

Participating subjects underwent brain MRI at 1.5T; DTI metrics from segmented masks of caudate, putamen, globus pallidus and thalamus were extracted from three timepoints with 6-year gaps. Patients underwent clinical assessment, including Unified Parkinson Disease Rating Scale Part 3 (UPDRS-III) and Hoehn and Yahr (H&Y) staging. A multivariate linear mixed-effects regression model with adjustments for age and gender was used to assess between-group differences in DTI metrics at each timepoint. Partial Pearson correlation analysis was used to correlate clinical motor scores with DTI metrics over time.

Results

MD progressively increased over time and was higher in the putamen (p < 0.001) and globus pallidus (p = 0.002). FA increased (p < 0.05) in the thalamus at year six, and decreased in the putamen and globus pallidus at year 12. Putaminal (p = 0.0210), pallidal (p = 0.0066) and caudate MD (p < 0.0001) correlated with disease duration. Caudate MD (p < 0.05) also correlated with UPDRS-III and H&Y scores.

Conclusion

Pallido-putaminal MD showed differential neurodegeneration in PD over 12 years on longitudinal DTI; putaminal and thalamic FA changes were complex. Caudate MD could serve as a surrogate marker to track late PD progression.

Processing of sensory, painful and vestibular stimuli in the thalamus

OBJECTIVES

The thalamus plays an important role in the mediation and integration of various stimuli (e.g., somatosensory, pain, and vestibular). Whether a stimulus-specific and topographic organization of the thalamic nuclei exists is still unknown. The aim of our study was to define a functional, in vivo map of multimodal sensory processing within the human thalamus.

METHODS

Twenty healthy individuals (10 women, 21-34 years old) participated. Defined sensory stimuli were applied to both hands (innocuous touch, mechanical pain, and heat pain) and the vestibular organ (galvanic stimulation) during 3 T functional MRI.

RESULTS

Bilateral thalamic activations could be detected for touch, mechanical pain, and vestibular stimulation within the left medio-dorsal and right anterior thalamus. Heat pain did not lead to thalamic activation at all. Stimuli applied to the left body side resulted in stronger activation patterns. Comparing an early with a late stimulation interval, the mentioned activation patterns were far more pronounced within the early stimulation interval.

CONCLUSIONS

The right anterior and ventral-anterior nucleus and the left medio-dorsal nucleus appear to be important for the processing of multimodal sensory information. In addition, galvanic stimulation is processed more laterally compared to mechanical pain. The observed changes in activity within the thalamic nuclei depending on the stimulation interval suggest that the stimuli are processed in a thalamic network rather than a distinct nucleus. In particular, the vestibular network within the thalamus recruits bilateral nuclei, rendering the thalamus an important integrative structure for vestibular function.

DORIS: A diffusion MRI-based 10 tissue class deep learning segmentation algorithm tailored to improve anatomically-constrained tractography

Modern tractography algorithms such as anatomically-constrained tractography (ACT) are based on segmentation maps of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). These maps are generally estimated from a T1-weighted (T1w) image and then registered in diffusion weighted images (DWI) space. Registration of T1w to diffusion space and partial volume estimation are challenging and rarely voxel-perfect. Diffusion-based segmentation would, thus, potentially allow not to have higher quality anatomical priors injected in the tractography process. On the other hand, even if FA-based tractography is possible without T1 registration, the literature shows that this technique suffers from multiple issues such as holes in the tracking mask and a high proportion of generated broken and anatomically implausible streamlines. Therefore, there is an important need for a tissue segmentation algorithm that works directly in the native diffusion space. We propose DORIS, a DWI-based deep learning segmentation algorithm. DORIS outputs 10 different tissue classes including WM, GM, CSF, ventricles, and 6 other subcortical structures (putamen, pallidum, hippocampus, caudate, amygdala, and thalamus). DORIS was trained and validated on a wide range of subjects, including 1,000 individuals from 22 to 90 years old from clinical and research DWI acquisitions, from 5 public databases. In the absence of a "true" ground truth in diffusion space, DORIS used a silver standard strategy from Freesurfer output registered onto the DWI. This strategy is extensively evaluated and discussed in the current study. Segmentation maps provided by DORIS are quantitatively compared to Freesurfer and FSL-fast and the impacts on tractography are evaluated. Overall, we show that DORIS is fast, accurate, and reproducible and that DORIS-based tractograms produce bundles with a longer mean length and fewer anatomically implausible streamlines.

A multi-scale probabilistic atlas of the human connectome

The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.

Convolutional Neural Network Based Frameworks for Fast Automatic Segmentation of Thalamic Nuclei from Native and Synthesized Contrast Structural MRI

Thalamic nuclei have been implicated in several neurological diseases. Thalamic nuclei parcellation from structural MRI is challenging due to poor intra-thalamic nuclear contrast while methods based on diffusion and functional MRI are affected by limited spatial resolution and image distortion. Existing multi-atlas based techniques are often computationally intensive and time-consuming. In this work, we propose a 3D convolutional neural network (CNN) based framework for thalamic nuclei parcellation using T1-weighted Magnetization Prepared Rapid Gradient Echo (MPRAGE) images. Transformation of images to an efficient representation has been proposed to improve the performance of subsequent classification tasks especially when working with limited labeled data. We investigate this by transforming the MPRAGE images to White-Matter-nulled MPRAGE (WMn-MPRAGE) contrast, previously shown to exhibit good intra-thalamic nuclear contrast, prior to the segmentation step. We trained two 3D segmentation frameworks using MPRAGE images (n = 35 subjects): (a) a native contrast segmentation (NCS) on MPRAGE images and (b) a synthesized contrast segmentation (SCS) where synthesized WMn-MPRAGE representation generated by a contrast synthesis CNN were used. Thalamic nuclei labels were generated using THOMAS, a multi-atlas segmentation technique proposed for WMn-MPRAGE images. The segmentation accuracy and clinical utility were evaluated on a healthy cohort (n = 12) and a cohort (n = 45) comprising of healthy subjects and patients with alcohol use disorder (AUD), respectively. Both the segmentation CNNs yielded comparable performances on most thalamic nuclei with Dice scores greater than 0.84 for larger nuclei and at least 0.7 for smaller nuclei. However, for some nuclei, the SCS CNN yielded significant improvements in Dice scores (medial geniculate nucleus, P = 0.003, centromedian nucleus, P = 0.01) and percent volume difference (ventral anterior, P = 0.001, ventral posterior lateral, P = 0.01) over NCS. In the AUD cohort, the SCS CNN demonstrated a significant atrophy in ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P = 0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS CNN showed spurious atrophy of the ventral posterior lateral nucleus. CNN-based segmentation of thalamic nuclei provides a fast and automated technique for thalamic nuclei prediction in MPRAGE images. The transformation of images to an efficient representation, such as WMn-MPRAGE, can provide further improvements in segmentation performance.

Altered thalamic subregion functional networks in patients with treatment-resistant schizophrenia

BACKGROUND

The thalamus plays a key role in filtering information and has extensive interconnectivity with other brain regions. A large body of evidence points to impaired functional connectivity (FC) of the thalamocortical pathway in schizophrenia. However, the functional network of the thalamic subregions has not been investigated in patients with treatment-resistant schizophrenia (TRS).

AIM

To identify the neural mechanisms underlying TRS, we investigated FC of thalamic sub-regions with cortical networks and voxels, and the associations of this FC with clinical symptoms. We hypothesized that the FC of thalamic sub-regions with cortical networks and voxels would differ between TRS patients and HCs.

METHODS

In total, 50 patients with TRS and 61 healthy controls (HCs) matched for age, sex, and education underwent resting-state functional magnetic resonance imaging (rs-fMRI) and clinical evaluation. Based on the rs-fMRI data, we conducted a FC analysis between thalamic subregions and cortical functional networks and voxels, and within thalamic subregions and cortical functional networks, in the patients with TRS. A functional parcellation atlas was used to segment the thalamus into nine subregions. Correlations between altered FC and TRS symptoms were explored.

RESULTS

We found differences in FC within thalamic subregions and cortical functional networks between patients with TRS and HCs. In addition, increased FC was observed between thalamic subregions and the sensorimotor cortex, frontal medial cortex, and lingual gyrus. These abnormalities were associated with the pathophysiology of TRS.

CONCLUSION

Our findings suggest that disrupted FC within thalamic subregions and cortical functional networks, and within the thalamocortical pathway, has potential as a marker for TRS. Our findings also improve our understanding of the relationship between the thalamocortical pathway and TRS symptoms.

Dorsal-to-ventral imbalance in the superior longitudinal fasciculus mediates methylphenidate's effect on beta oscillations in ADHD

While pharmacological treatment with methylphenidate (MPH) is a first line intervention for ADHD, its mechanisms of action have yet to be elucidated. We here seek to identify the white matter tracts that mediate MPH's effect on beta oscillations. We implemented a double-blind placebo-controlled crossover design, where boys diagnosed with ADHD underwent behavioral and MEG measurements during a spatial attention task while on and off MPH. The results were compared with an age/IQ-matched control group. Estimates of white matter tracts were obtained using diffusion tensor imaging (DTI). Via a stepwise model selection strategy, we identified the fiber tracts (regressors) significantly predicting values of the dependent variables of interest (i.e., oscillatory power, behavioral performance, and clinical symptoms): the anterior thalamic radiation (ATR), the superior longitudinal fasciculus ("parietal endings") (SLFp), and superior longitudinal fasciculus ("temporal endings") (SLFt). ADHD symptoms severity was associated with lower fractional anisotropy (FA) within the ATR. In addition, individuals with relatively higher FA in SLFp compared to SLFt, led to stronger behavioral effects of MPH in the form of faster and more accurate responses. Furthermore, the same parietotemporal FA gradient explained the effects of MPH on beta modulation: subjects with ADHD exhibiting higher FA in SLFp compared to SLFt also displayed greater effects of MPH on beta power during response preparation. Our data suggest that the behavioral deficits and aberrant oscillatory modulations observed in ADHD depend on a possibly detrimental structural connectivity imbalance within the SLF, caused by a diffusivity gradient in favor of parietal rather than temporal, fiber tracts.

Automatic segmentation of Deep Grey Nuclei using a high-resolution 7T MRI Atlas - quantification of T1 values in healthy volunteers

We present a new consensus atlas of deep grey nuclei obtained by shape-based averaging of manual segmentation of two experienced neuroradiologists and optimized from 7T MP2RAGE images acquired at (0.6mm)³ in 60 healthy subjects. A group-wise normalization method was used to build a high-contrast and high-resolution T₁ -weighted brain template (0.5mm)³ using data from 30 out of the 60 controls. Delineation of 24 deep grey nuclei per hemisphere, including the claustrum and twelve thalamic nuclei, was then performed by two expert neuroradiologists and reviewed by a third neuroradiologist according to tissue contrast and external references based on the Morel atlas. Corresponding deep grey matter structures were also extracted from the Morel and CIT168 atlases. The data-derived, Morel and CIT168 atlases were all applied at the individual level using non-linear registration to fit the subject reference and to extract absolute mean quantitative T₁ values derived from the 3D-MP2RAGE volumes, after correction for residual B₁ ⁺ biases. Three metrics (The Dice and the volumetric similarity coefficients, and a novel Hausdorff distance) were used to estimate the inter-rater agreement of manual MRI segmentation and inter-atlas variability, and these metrics were measured to quantify biases due to image registration and their impact on the measurements of the quantitative T₁ values was highlighted. This represents a fully-automated segmentation process permitting the extraction of unbiased normative T₁ values in a population of young healthy controls as a reference for characterizing subtle structural alterations of deep grey nuclei relevant to a range of neurological diseases.

In vivo high-resolution structural MRI-based atlas of human thalamic nuclei

Thalamic nuclei play critical roles in regulation of neurological functions like sleep and wakefulness. They are increasingly implicated in neurodegenerative and neurological diseases such as multiple sclerosis and essential tremor. However, segmentation of thalamic nuclei is difficult due to their poor visibility in conventional MRI scans. Sophisticated methods have been proposed which require specialized MRI acquisitions and complex post processing. There are few high spatial resolution (1 mm3 or higher) in vivo MRI thalamic atlases available currently. The goal of this work is the development of an in vivo MRI-based structural thalamic atlas at 0.7 × 0.7 × 0.5 mm resolution based on manual segmentation of 9 healthy subjects using the Morel atlas as a guide. Using data analysis from healthy subjects as well as patients with multiple-sclerosis and essential tremor and at 3T and 7T MRI, we demonstrate the utility of this atlas to provide fast and accurate segmentation of thalamic nuclei when only conventional T1 weighted images are available.

Structural assessment of thalamus morphology in brain disorders: A review and recommendation of thalamic nucleus segmentation and shape analysis

The thalamus is a central brain structure crucially involved in cognitive, emotional, sensory, and motor functions and is often reported to be involved in the pathophysiology of neurological and psychiatric disorders. The functional subdivision of the thalamus warrants morphological investigation on the level of individual subnuclei. In addition to volumetric measures, the investigation of other morphological features may give additional insights into thalamic morphology. For instance, shape features offer a higher spatial resolution by revealing small, regional differences that are left undetected in volumetric analyses. In this review, we discuss the benefits and limitations of recent advances in neuroimaging techniques to investigate thalamic morphology in vivo, leading to our proposed methodology. This methodology consists of available pipelines for volume and shape analysis, focussing on the morphological features of volume, thickness, and surface area. We demonstrate this combined approach in a Parkinson's disease cohort to illustrate their complementarity. Considering our findings, we recommend a combined methodology as it allows for more sensitive investigation of thalamic morphology in clinical populations.

Thalamic volume and fear extinction interact to predict acute posttraumatic stress severity

Posttraumatic stress disorder (PTSD) is associated with lower gray matter volume (GMV) in brain regions critical for extinction of learned threat. However, relationships among volume, extinction learning, and PTSD symptom development remain unclear. We investigated subcortical brain volumes in regions supporting extinction learning and fear-potentiated startle (FPS) to understand brain-behavior interactions that may impact PTSD symptom development in recently traumatized individuals. Participants (N = 99) completed magnetic resonance imaging and threat conditioning two weeks following trauma exposure as part of a multisite observational study to understand the neuropsychiatric effects of trauma (AURORA Study). Participants completed self-assessments of PTSD (PTSD Checklist for DSM-5; PCL-5), dissociation, and depression symptoms two- and eight-weeks post-trauma. We completed multiple regressions to investigate relationships between FPS during late extinction, GMV, and PTSD symptom development. The interaction between thalamic GMV and FPS during late extinction at two weeks post-trauma predicted PCL-5 scores eight weeks (t (75) = 2.49, β = 0.28, p = 0.015) post-trauma. Higher FPS predicted higher PCL-5 scores in the setting of increased thalamic GMV. Meanwhile, lower FPS also predicted higher PCL-5 scores in the setting of decreased thalamic GMV. Thalamic GMV and FPS interactions also predicted posttraumatic dissociative and depressive symptoms. Amygdala and hippocampus GMV by FPS interactions were not associated with posttraumatic symptom development. Taken together, thalamic GMV and FPS during late extinction interact to contribute to adverse posttraumatic neuropsychiatric outcomes. Multimodal assessments soon after trauma have the potential to distinguish key phenotypes vulnerable to posttraumatic neuropsychiatric outcomes.

In Vivo Super-Resolution Track-Density Imaging for Thalamic Nuclei Identification

The development of novel techniques for the in vivo, non-invasive visualization and identification of thalamic nuclei has represented a major challenge for human neuroimaging research in the last decades. Thalamic nuclei have important implications in various key aspects of brain physiology and many of them show selective alterations in various neurologic and psychiatric disorders. In addition, both surgical stimulation and ablation of specific thalamic nuclei have been proven to be useful for the treatment of different neuropsychiatric diseases. The present work aimed at describing a novel protocol for histologically guided delineation of thalamic nuclei based on short-tracks track-density imaging (stTDI), which is an advanced imaging technique exploiting high angular resolution diffusion tractography to obtain super-resolved white matter maps. We demonstrated that this approach can identify up to 13 distinct thalamic nuclei bilaterally with very high inter-subject (ICC: 0.996, 95% CI: 0.993-0.998) and inter-rater (ICC:0.981; 95% CI:0.963-0.989) reliability, and that both subject-based and group-level thalamic parcellation show a fair share of similarity to a recent standard-space histological thalamic atlas. Finally, we showed that stTDI-derived thalamic maps can be successfully employed to study structural and functional connectivity of the thalamus and may have potential implications both for basic and translational research, as well as for presurgical planning purposes.

Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions

Background/Objective

For patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis.

Methods

We consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development.

Results

Knowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints.

Conclusions

This personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12028-021-01227-y.

Structural Changes in Thalamic Nuclei Across Prodromal and Clinical Alzheimer's Disease

BACKGROUND

Increasing evidence suggests that thalamic nuclei may atrophy in Alzheimer's disease (AD). We hypothesized that there will be significant atrophy of limbic thalamic nuclei associated with declining memory and cognition across the AD continuum.

OBJECTIVE

The objective of this work was to characterize volume differences in thalamic nuclei in subjects with early and late mild cognitive impairment (MCI) as well as AD when compared to healthy control (HC) subjects using a novel MRI-based thalamic segmentation technique (THOMAS).

METHODS

MPRAGE data from the ADNI database were used in this study (n = 540). Healthy control (n = 125), early MCI (n = 212), late MCI (n = 114), and AD subjects (n = 89) were selected, and their MRI data were parcellated to determine the volumes of 11 thalamic nuclei for each subject. Volumes across the different clinical subgroups were compared using ANCOVA.

RESULTS

There were significant differences in thalamic nuclei volumes between HC, late MCI, and AD subjects. The anteroventral, mediodorsal, pulvinar, medial geniculate, and centromedian nuclei were significantly smaller in subjects with late MCI and AD when compared to HC subjects. Furthermore, the mediodorsal, pulvinar, and medial geniculate nuclei were significantly smaller in early MCI when compared to HC subjects.

CONCLUSION

This work highlights nucleus specific atrophy within the thalamus in subjects with early and late MCI and AD. This is consistent with the hypothesis that memory and cognitive changes in AD are mediated by damage to a large-scale integrated neural network that extends beyond the medial temporal lobes.

Individualized Thalamic Parcellation Reveals Alterations in Shape and Microstructure of Thalamic Nuclei in Patients with Disorder of Consciousness

The thalamus plays crucial roles in consciousness generation and information processing. Previous evidence suggests that disorder of consciousness (DOC) caused by severe brain injury, is potentially related to thalamic abnormalities. However, how the morphology and microstructure change in thalamic subfields and thalamocortical fiber pathways in patients with DOC, and the relationships between these changes and the consciousness status remain unclear. Here, we generated the individual-specific thalamic parcellation in 10 DOC patients and 10 healthy controls (HC) via a novel thalamic segmentation framework based on the fiber orientation distribution (FOD) derived from 7-Tesla diffusion MRI, and investigated the shape deformation of thalamic nuclei as well as the microstructural changes associated with thalamic nuclei and thalamocortical pathways in patients with DOC. Enlargement of dorsal posterior nucleus and atrophy of anterior nucleus in the right thalamus were observed in DOC cohort relative to the HCs, and the former was closely linked to the consciousness level of the patients. We also found significant reductions of fiber density, but not fiber bundle cross-section, within several thalamic nuclei and most of the thalamocortical fiber pathways, suggesting that loss of axons might take primary responsibility for the impaired thalamocortical connections in patients with DOC rather than the change in fiber-bundle morphology. Furthermore, the individual-specific thalamic parcellation achieved 80% accuracy in classifying patients at the minimally conscious state from the vegetative state, compared with ~60% accuracy based on group-level parcellations. Our findings provide the first evidence for the shape deformation of thalamic nuclei in DOC patients and the microstructural basis of the disrupted thalamocortical connections.

Discriminating subcortical ischemic vascular disease and Alzheimer's disease by diffusion kurtosis imaging in segregated thalamic regions

Differentiating between subcortical ischemic vascular disease (SIVD), Alzheimer's disease (AD), and normal cognition (NC) remains a challenge, and reliable neuroimaging biomarkers are needed. The current study, therefore, investigated the discriminative ability of diffusion kurtosis imaging (DKI) metrics in segregated thalamic regions and compare with diffusion tensor imaging (DTI) metrics. Twenty‐three SIVD patients, 30 AD patients, and 24 NC participants underwent brain magnetic resonance imaging. The DKI metrics including mean kurtosis (MK), axial kurtosis (K_axial) and radial kurtosis (K_radial) and the DTI metrics including diffusivity and fractional anisotropy (FA) were measured within the whole thalamus and segregated thalamic subregions. Strategic correlations by group, thalamo‐frontal connectivity, and canonical discriminant analysis (CDA) were used to demonstrate the discriminative ability of DKI for SIVD, AD, and NC. Whole and segregated thalamus analysis suggested that DKI metrics are less affected by white matter hyperintensities compared to DTI metrics. Segregated thalamic analysis showed that MK and K_radial were notably different between SIVD and AD/NC. The correlation analysis between K_axial and MK showed a nonsignificant relationship in SIVD group, a trend of negative relationship in AD group, and a significant positive relationship in NC group. A wider spatial distribution of thalamo‐frontal connectivity differences across groups was shown by MK compared to FA. CDA showed a discriminant power of 97.4% correct classification using all DKI metrics. Our findings support that DKI metrics could be more sensitive than DTI metrics to reflect microstructural changes within the gray matter, hence providing complementary information for currently outlined pathogenesis of SIVD and AD.

Automated diffusion-based parcellation of the hypothalamus reveals subunit-specific associations with obesity

The hypothalamus is a small, yet highly versatile structure mainly involved in bodily functions such as control of food intake and endocrine activity. Functional anatomy of different hypothalamic areas is mainly investigated using structural MRI, validated by ex-vivo histological studies. Based on diffusion-weighted imaging (DWI), recent automated clustering methods provide robust tools for parcellation. Using data of 100 healthy adults provided by the Human Connectome Project Database, we applied DWI-based automated clustering to the hypothalamus and related microstructural properties in these hypothalamic compartments to obesity. Our results suggest that the hypothalamus can be reliably partitioned into four clusters in each hemisphere using diffusion-based parcellation. These correspond to an anterior–superior, anterior-inferior, intermediate, and posterior cluster. Obesity was predicted by mean diffusivity of the anterior–superior cluster, suggesting altered inhibition of food intake. The proposed method provides an automated hypothalamic parcellation technique based on DWI data to explore anatomy and function of hypothalamic subunits in vivo in humans.

Fast automatic segmentation of thalamic nuclei from MP2RAGE acquisition at 7 Tesla

PURPOSE

Thalamic nuclei are largely invisible in conventional MRI due to poor contrast. Thalamus Optimized Multi-Atlas Segmentation (THOMAS) provides automatic segmentation of 12 thalamic nuclei using white-matter-nulled (WMn) Magnetization Prepared Rapid Gradient Echo (MPRAGE) sequence at 7T, but increases overall scan duration. Routinely acquired, bias-corrected Magnetization Prepared 2 Rapid Gradient Echo (MP2RAGE) sequence yields superior tissue contrast and quantitative T1 maps. Application of THOMAS to MP2RAGE has been investigated in this study.

METHODS

Eight healthy volunteers and five pediatric-onset multiple sclerosis patients were recruited at Children's Hospital of Philadelphia and scanned at Siemens 7T with WMn-MPRAGE and multi-echo-MP2RAGE (ME-MP2RAGE) sequences. White-matter-nulled contrast was synthesized (MP2-SYN) from T₁ maps from ME-MP2RAGE sequence. Thalamic nuclei were segmented using THOMAS joint label fusion algorithm from WMn-MPRAGE and MP2-SYN datasets. THOMAS pipeline was modified to use majority voting to segment bias corrected T1-weighted uniform (MP2-UNI) images. Thalamic nuclei from MP2-SYN and MP2-UNI images were evaluated against corresponding nuclei obtained from WMn-MPRAGE images using dice coefficients, volume similarity indices (VSIs) and distance between centroids.

RESULTS

For MP2-SYN, dice > 0.85 and VSI > 0.95 was achieved for five larger nuclei and dice > 0.6 and VSI > 0.7 was achieved for seven smaller nuclei. The dice and VSI were slightly higher, whereas the distance between centroids were smaller for MP2-SYN compared to MP2-UNI, indicating improved performance using the MP2-SYN image.

CONCLUSIONS

THOMAS algorithm can successfully segment thalamic nuclei in MP2RAGE images with essentially equivalent quality as WMn-MPRAGE, widening its applicability in studies focused on thalamic involvement in aging and disease.

Automated thalamic nuclei segmentation using multi-planar cascaded convolutional neural networks

PURPOSE

To develop a fast and accurate convolutional neural network based method for segmentation of thalamic nuclei.

METHODS

A cascaded multi-planar scheme with a modified residual U-Net architecture was used to segment thalamic nuclei on conventional and white-matter-nulled (WMn) magnetization prepared rapid gradient echo (MPRAGE) data. A single network was optimized to work with images from healthy controls and patients with multiple sclerosis (MS) and essential tremor (ET), acquired at both 3 T and 7 T field strengths. WMn-MPRAGE images were manually delineated by a trained neuroradiologist using the Morel histological atlas as a guide to generate reference ground truth labels. Dice similarity coefficient and volume similarity index (VSI) were used to evaluate performance. Clinical utility was demonstrated by applying this method to study the effect of MS on thalamic nuclei atrophy.

RESULTS

Segmentation of each thalamus into twelve nuclei was achieved in under a minute. For 7 T WMn-MPRAGE, the proposed method outperforms current state-of-the-art on patients with ET with statistically significant improvements in Dice for five nuclei (increase in the range of 0.05-0.18) and VSI for four nuclei (increase in the range of 0.05-0.19), while performing comparably for healthy and MS subjects. Dice and VSI achieved using 7 T WMn-MPRAGE data are comparable to those using 3 T WMn-MPRAGE data. For conventional MPRAGE, the proposed method shows a statistically significant Dice improvement in the range of 0.14-0.63 over FreeSurfer for all nuclei and disease types. Effect of noise on network performance shows robustness to images with SNR as low as half the baseline SNR. Atrophy of four thalamic nuclei and whole thalamus was observed for MS patients compared to healthy control subjects, after controlling for the effect of parallel imaging, intracranial volume, gender, and age (p < 0.004).

CONCLUSION

The proposed segmentation method is fast, accurate, performs well across disease types and field strengths, and shows great potential for improving our understanding of thalamic nuclei involvement in neurological diseases.

Partial-volume modeling reveals reduced gray matter in specific thalamic nuclei early in the time course of psychosis and chronic schizophrenia

The structural complexity of the thalamus, due to its mixed composition of gray and white matter, make it challenging to disjoint and quantify each tissue contribution to the thalamic anatomy. This work promotes the use of partial‐volume‐based over probabilistic‐based tissue segmentation approaches to better capture thalamic gray matter differences between patients at different stages of psychosis (early and chronic) and healthy controls. The study was performed on a cohort of 23 patients with schizophrenia, 41 with early psychosis and 69 age and sex‐matched healthy subjects. Six tissue segmentation approaches were employed to obtain the gray matter concentration/probability images. The statistical tests were applied at three different anatomical scales: whole thalamus, thalamic subregions and voxel‐wise. The results suggest that the partial volume model estimation of gray matter is more sensitive to detect atrophies within the thalamus of patients with psychosis. However all the methods detected gray matter deficit in the pulvinar, particularly in early stages of psychosis. This study demonstrates also that the gray matter decrease varies nonlinearly with age and between nuclei. While a gray matter loss was found in the pulvinar of patients in both stages of psychosis, reduced gray matter in the mediodorsal was only observed in early psychosis subjects. Finally, our analyses point to alterations in a sub‐region comprising the lateral posterior and ventral posterior nuclei. The obtained results reinforce the hypothesis that thalamic gray matter assessment is more reliable when the tissues segmentation method takes into account the partial volume effect.

A systematic comparison of structural-, structural connectivity-, and functional connectivity-based thalamus parcellation techniques

The thalamus consists of several histologically and functionally distinct nuclei increasingly implicated in brain pathology and important for treatment, motivating the need for development of fast and accurate thalamic parcellation. The contrast between thalamic nuclei as well as between the thalamus and surrounding tissues is poor in T1- and T2-weighted magnetic resonance imaging (MRI), inhibiting efforts to date to segment the thalamus using standard clinical MRI. Automatic parcellation techniques have been developed to leverage thalamic features better captured by advanced MRI methods, including magnetization prepared rapid acquisition gradient echo (MP-RAGE), diffusion tensor imaging (DTI), and resting-state functional MRI (fMRI). Despite operating on fundamentally different image contrasts, these methods claim a high degree of agreement with the Morel stereotactic atlas of the thalamus. However, no comparison has been undertaken to compare the results of these disparate parcellation methods. We have implemented state-of-the-art structural-, diffusion-, and functional imaging-based thalamus parcellation techniques and used them on a single set of subjects. We present the first systematic qualitative and quantitative comparison of these methods. The results show that DTI parcellation agrees more with structural parcellation in the larger thalamic nuclei, while rsfMRI parcellation agrees more with structural parcellation in the smaller nuclei. Structural parcellation is the most accurate in the delineation of small structures such as the habenular, antero-ventral, and medial geniculate nuclei.

Advanced Imaging and Direct Targeting of the Motor Thalamus and Dentato-Rubro-Thalamic Tract for Tremor: A Systematic Review

Direct targeting methods for stereotactic neurosurgery in the treatment of essential tremor have been the subject of active research over the past decade but have not yet been systematically reviewed. We present a clinically oriented topic review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses Group guidelines. Our focus is studies using advanced magnetic resonance imaging (MRI) techniques (ultrahigh-field structural MRI, diffusion-weighted imaging, diffusion-tensor tractography, and functional MRI) for patient specific, in vivo identification of the ventral intermediate nucleus and the dentato-rubro-thalamic tract.

Generation of human thalamus atlases from 7 T data and application to intrathalamic nuclei segmentation in clinical 3 T T1-weighted images

The thalamus serves as the central relay station for the brain. It processes and relays sensory and motor signals between different subcortical regions and the cerebral cortex and it can be divided into several neuronal clusters referred to as nuclei. Each of these can possibly be subdivided into sub-nuclei. Accurate and reliable identification of thalamic nuclei is important for surgical interventions and neuroanatomical studies. This is however a challenging task because the small size of the nuclei and the lack of contrast over the thalamus region in clinically acquired images does not permit the visualization of their boundaries. A number of methods have been developed for thalamus parcellation but the vast majority of these relies on diffusion imaging or functional imaging. The low resolution of these images only permit localizing the largest nuclei. In this work we propose a method to segment smaller nuclei. We first present a protocol to build histological-like atlases from a series of high-field (7 Tesla) MR images acquired with different pulse sequences that each permits to visualize the boundaries of a subset of the nuclei. We use this protocol to scan 9 subjects and we manually delineate 23 thalamic nuclei following the Morel atlas naming convention for each of these subjects. Manual contours for the nuclei are subsequently utilized to create statistical shape models. With these data, we compare four methods for the segmentation of thalamic nuclei in 3 T images we have also acquired for the 9 subjects included in the study: (1) single atlas, (2) multi atlas, (3) statistical shape, and (4) hierarchical statistical shape in which thalamic nuclei are hierarchically fitted to the images, starting from the largest ones. Results of a leave-one-out validation study conducted on the nine image sets we have acquired show that the multi atlas approach improves upon the single atlas approach for most nuclei. Segmentations obtained with the hierarchical statistical shape model yield the highest accuracy, with dice coefficients ranging from 0.53 to 0.90, mean surface errors from 0.27 mm to 0.64 mm, and maximum surface errors from 1.31 mm to 2.52 mm for all nuclei averaged across test cases. This suggests the feasibility of using such approach for localizing thalamic substructures in clinically acquired MR volumes. It may have a direct impact on surgeries such as Deep Brain Stimulation procedures that require the implantation of stimulating electrodes in specific thalamic nuclei.

Quantifying deep grey matter atrophy using automated segmentation approaches: A systematic review of structural MRI studies

The deep grey matter (DGM) nuclei of the brain play a crucial role in learning, behaviour, cognition, movement and memory. Although automated segmentation strategies can provide insight into the impact of multiple neurological conditions affecting these structures, such as Multiple Sclerosis (MS), Huntington's disease (HD), Alzheimer's disease (AD), Parkinson's disease (PD) and Cerebral Palsy (CP), there are a number of technical challenges limiting an accurate automated segmentation of the DGM. Namely, the insufficient contrast of T1 sequences to completely identify the boundaries of these structures, as well as the presence of iso-intense white matter lesions or extensive tissue loss caused by brain injury. Therefore in this systematic review, 269 eligible studies were analysed and compared to determine the optimal approaches for addressing these technical challenges. The automated approaches used among the reviewed studies fall into three broad categories, atlas-based approaches focusing on the accurate alignment of atlas priors, algorithmic approaches which utilise intensity information to a greater extent, and learning-based approaches that require an annotated training set. Studies that utilise freely available software packages such as FIRST, FreeSurfer and LesionTOADS were also eligible, and their performance compared. Overall, deep learning approaches achieved the best overall performance, however these strategies are currently hampered by the lack of large-scale annotated data. Improving model generalisability to new datasets could be achieved in future studies with data augmentation and transfer learning. Multi-atlas approaches provided the second-best performance overall, and may be utilised to construct a "silver standard" annotated training set for deep learning. To address the technical challenges, providing robustness to injury can be improved by using multiple channels, highly elastic diffeomorphic transformations such as LDDMM, and by following atlas-based approaches with an intensity driven refinement of the segmentation, which has been done with the Expectation Maximisation (EM) and level sets methods. Accounting for potential lesions should be achieved with a separate lesion segmentation approach, as in LesionTOADS. Finally, to address the issue of limited contrast, R2*, T2* and QSM sequences could be used to better highlight the DGM due to its higher iron content. Future studies could look to additionally acquire these sequences by retaining the phase information from standard structural scans, or alternatively acquiring these sequences for only a training set, allowing models to learn the "improved" segmentation from T1-sequences alone.

Evidence for altered neurodevelopment and neurodegeneration in Wolfram syndrome using longitudinal morphometry

Wolfram syndrome is a rare disease caused by mutations in the WFS1 gene leading to symptoms in early to mid-childhood. Brain structural abnormalities are present even in young children, but it is not known when these abnormalities arise. Such information is critical in determining optimal outcome measures for clinical trials and in understanding the aberrant neurobiological processes in Wolfram syndrome. Using voxel-wise and regional longitudinal analyses, we compared brain volumes in Wolfram patients (n = 29; ages 5–25 at baseline; mean follow-up = 3.6 years), to age and sex-equivalent controls (n = 52; ages 6–26 at baseline; mean follow-up = 2.0 years). Between groups, white and gray matter volumes were affected differentially during development. Controls had uniformly increasing volume in white matter, whereas the Wolfram group had stable (optic radiations) or decreasing (brainstem, ventral pons) white matter volumes. In gray matter, controls had stable (thalamus, cerebellar cortex) or decreasing volumes (cortex), whereas the Wolfram group had decreased volume in thalamus and cerebellar cortex. These patterns suggest that there may be early, stalled white matter development in Wolfram syndrome, with additional degenerative processes in both white and gray matter. Ideally, animal models could be used to identify the underlying mechanisms and develop specific interventions.

Thalamus Optimized Multi Atlas Segmentation (THOMAS): fast, fully automated segmentation of thalamic nuclei from structural MRI

The thalamus and its nuclei are largely indistinguishable on standard T1 or T2 weighted MRI. While diffusion tensor imaging based methods have been proposed to segment the thalamic nuclei based on the angular orientation of the principal diffusion tensor, these are based on echo planar imaging which is inherently limited in spatial resolution and suffers from distortion. We present a multi-atlas segmentation technique based on white-matter-nulled MP-RAGE imaging that segments the thalamus into 12 nuclei with computation times on the order of 10 min on a desktop PC; we call this method THOMAS (THalamus Optimized Multi Atlas Segmentation). THOMAS was rigorously evaluated on 7T MRI data acquired from healthy volunteers and patients with multiple sclerosis by comparing against manual segmentations delineated by a neuroradiologist, guided by the Morel atlas. Segmentation accuracy was very high, with uniformly high Dice indices: at least 0.85 for large nuclei like the pulvinar and mediodorsal nuclei and at least 0.7 even for small structures such as the habenular, centromedian, and lateral and medial geniculate nuclei. Volume similarity indices ranged from 0.82 for the smaller nuclei to 0.97 for the larger nuclei. Volumetry revealed that the volumes of the right anteroventral, right ventral posterior lateral, and both right and left pulvinar nuclei were significantly lower in MS patients compared to controls, after adjusting for age, sex and intracranial volume. Lastly, we evaluated the potential of this method for targeting the Vim nucleus for deep brain surgery and focused ultrasound thalamotomy by overlaying the Vim nucleus segmented from pre-operative data on post-operative data. The locations of the ablated region and active DBS contact corresponded well with the segmented Vim nucleus. Our fast, direct structural MRI based segmentation method opens the door for MRI guided intra-operative procedures like thalamotomy and asleep DBS electrode placement as well as for accurate quantification of thalamic nuclear volumes to follow progression of neurological disorders.

White-matter-nulled MPRAGE at 7T reveals thalamic lesions and atrophy of specific thalamic nuclei in multiple sclerosis

BACKGROUND

Investigating the degeneration of specific thalamic nuclei in multiple sclerosis (MS) remains challenging.

METHODS

White-matter-nulled (WMn) MPRAGE, MP-FLAIR, and standard T1-weighted magnetic resonance imaging (MRI) were performed on MS patients (n = 15) and matched controls (n = 12). Thalamic lesions were counted in individual sequences and lesion contrast-to-noise ratio (CNR) was measured. Volumes of 12 thalamic nuclei were measured using an automatic segmentation pipeline specifically developed for WMn-MPRAGE.

RESULTS

WMn-MPRAGE showed more thalamic MS lesions (n = 35 in 9 out of 15 patients) than MP-FLAIR (n = 25) and standard T1 (n = 23), which was associated with significant improvement of CNR (p < 0.0001). MS patients had whole thalamus atrophy (p = 0.003) with lower volumes found for the anteroventral (p < 0.001), the pulvinar (p < 0.0001), and the habenular (p = 0.004) nuclei.

CONCLUSION

WMn-MPRAGE and automatic thalamic segmentation can highlight thalamic MS lesions and measure patterns of focal thalamic atrophy.

Comparison of MRI-based automated segmentation methods and functional neurosurgery targeting with direct visualization of the Ventro-intermediate thalamic nucleus at 7T

The ventro-intermediate nucleus (Vim), as part of the motor thalamic nuclei, is a commonly used target in functional stereotactic neurosurgery for treatment of drug-resistant tremor. As it cannot be directly visualized on routinely used magnetic resonance imaging (MRI), its clinical targeting is performed using indirect methods. Recent literature suggests that the Vim can be directly visualized on susceptibility-weighted imaging (SWI) acquired at 7 T. Our work aims to assess the distinguishable Vim on 7 T SWI in both healthy-population and patients and, using it as a reference, to compare it with: (1) The clinical targeting, (2) The automated parcellation of thalamic subparts based on 3 T diffusion MRI (dMRI), and (3) The multi-atlas segmentation techniques. In 95.2% of the data, the manual outline was adjacent to the inferior lateral border of the dMRI-based motor-nuclei group, while in 77.8% of the involved cases, its ventral part enclosed the Guiot points. Moreover, the late MRI signature in the patients was always observed in the anterior part of the manual delineation and it overlapped with the multi-atlas outline. Overall, our study provides new insight on Vim discrimination through MRI and imply novel strategies for its automated segmentation, thereby opening new perspectives for standardizing the clinical targeting.

Nuclei-specific thalamic connectivity predicts seizure frequency in drug-resistant medial temporal lobe epilepsy

Background and objectives

We assessed correlations between the resting state functional connectivity (RSFC) of different thalamic nuclei and seizure frequency in patients with drug-resistant medial temporal lobe epilepsy (mTLE).

Methods

Seventeen patients with mTLE and 17 sex-/age-/handedness-matched controls participated. A seed-based correlation method for the resting-state FMRI data was implemented to get RSFC maps of 70 thalamic nuclei seed masks. Group statistics for individual RSFC for subjects and seed masks were performed to obtain within-group characteristics and between-group differences with age covariates. A linear regression was applied to test whether seizure frequency correlated with thalamic nuclear RSFC with the whole brain in mTLE patients.

Results

RSFC of thalamic nuclei showed spatially distinguishable connectivity patterns that reflected principal inputs and outputs that were derived from priori anatomical knowledge. We found group differences between normal control and mTLE groups in RSFC for nuclei seeds located in various subdivisions of thalamus. The RSFCs in some of those nuclei were strongly correlated with seizure frequency.

Conclusions

Mediodorsal thalamic nuclei may play important roles in seizure activity or in the regulation of neuronal activity in the limbic system. The RSFC of motor- and sensory-relay nuclei may help elucidate sensory-motor deficits associated with chronic seizure activity. RSFC of the pulvinar nuclei of the thalamus could also be a key reflection of symptom-related functional deficits in mTLE.

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We assessed the functional connectivity for 70 thalamic nuclei of temporal lobe epilepsy subjects.

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Each nucleus showed distinguishable connectivity along its known pathological preference.

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Especially, the mediodorsal thalamic nuclei may play important roles in seizure frequency.

A probabilistic atlas of the human thalamic nuclei combining ex vivo MRI and histology

The human thalamus is a brain structure that comprises numerous, highly specific nuclei. Since these nuclei are known to have different functions and to be connected to different areas of the cerebral cortex, it is of great interest for the neuroimaging community to study their volume, shape and connectivity in vivo with MRI. In this study, we present a probabilistic atlas of the thalamic nuclei built using ex vivo brain MRI scans and histological data, as well as the application of the atlas to in vivo MRI segmentation. The atlas was built using manual delineation of 26 thalamic nuclei on the serial histology of 12 whole thalami from six autopsy samples, combined with manual segmentations of the whole thalamus and surrounding structures (caudate, putamen, hippocampus, etc.) made on in vivo brain MR data from 39 subjects. The 3D structure of the histological data and corresponding manual segmentations was recovered using the ex vivo MRI as reference frame, and stacks of blockface photographs acquired during the sectioning as intermediate target. The atlas, which was encoded as an adaptive tetrahedral mesh, shows a good agreement with previous histological studies of the thalamus in terms of volumes of representative nuclei. When applied to segmentation of in vivo scans using Bayesian inference, the atlas shows excellent test-retest reliability, robustness to changes in input MRI contrast, and ability to detect differential thalamic effects in subjects with Alzheimer's disease. The probabilistic atlas and companion segmentation tool are publicly available as part of the neuroimaging package FreeSurfer.

In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging

The thalamic nuclei are involved in many neurodegenerative diseases and therefore, their identification is of key importance in numerous clinical treatments. Automated segmentation of thalamic subparts is currently achieved by exploring diffusion-weighted magnetic resonance imaging (DW-MRI), but in absence of such data, atlas-based segmentation can be used as an alternative. Currently, there is a limited number of available digital atlases of the thalamus. Moreover, all atlases are created using a few subjects only, thus are prone to errors due to the inter-subject variability of the thalamic morphology. In this work, we present a probabilistic atlas of anatomical subparts of the thalamus built upon a relatively large dataset where the individual thalamic parcellation was done by employing a recently proposed automatic diffusion-based clustering method. Our analyses, comparing the segmentation performance between the atlas-based and the clustering method, demonstrate the ability of the provided atlas to substitute the automated diffusion-based subdivision in the individual space when the DW-MRI is not available.