Automated thalamic nuclear segmentation has contributed towards a shift in neuroimaging analyses, from treating the thalamus as a homogeneous, passive relay, to a set of individual nuclei, embedded within distinct brain-wide circuits. However, many studies continue to widely rely on FreeSurfer’s segmentation of T1-weighted structural MRIs, despite their poor intrathalamic nuclear contrast. Meanwhile, a convolutional neural network tool has been developed for FreeSurfer, using information from both diffusion and T1-weighted MRIs. Another popular thalamic nuclear segmentation technique is HIPS-THOMAS, a multi-atlas-based method that leverages white-matter-like contrast synthesized from T1-weighted MRIs. However, comparisons amongst methods remain scant, and the thalamic atlases against which these methods have been assessed have their own limitations. These issues may compromise the quality of cross-species comparisons, structural and functional connectivity studies in health and disease, as well as the efficacy of neuromodulatory interventions targeting the thalamus. Here, we report, for the first time, comparisons amongst HIPS-THOMAS, the standard FreeSurfer segmentation, and its more recent development, against two thalamic atlases. We used two cohorts of healthy adults, and one cohort of patients in the chronic phase of autoimmune limbic encephalitis. In healthy adults, HIPS-THOMAS surpassed, not only the standard FreeSurfer segmentation, but also its more recent, diffusion-based update. The improvements made with the latter were limited to a few nuclei. Finally, the standard FreeSurfer method underperformed in distinguishing between patients and healthy controls based on the affected anteroventral and pulvinar nuclei. We provide recommendations on automated segmentation methods of the human thalamus using structural brain imaging.

The thalamus comprises multiple nuclei that support higher-order cognitive functions. However, its internal architecture remains difficult to delineate using conventional T1- or T2-weighted MRI because of limited tissue contrast. Diffusion-weighted MRI provides richer microstructural detail, yet accurate segmentation is still challenged by low anisotropy and tissue heterogeneity. To address these challenges, we present a modified spectral clustering framework for thalamic segmentation. Our approach jointly leverages voxel-wise information and fiber orientation distribution (FOD) features derived from multi-shell multi-tissue constrained spherical deconvolution. When evaluated using spatial probabilistic maps that capture across-subject spatial variability in labels, k-means and spectral clustering exhibit broadly similar group-level variability patterns. However, the spectral clustering framework accommodates smaller thalamic subdivisions, including the lateral and medial geniculate nuclei (LGN and MGN), which required exclusion from the k-means configuration for stable parcellation. Under these conditions, spectral clustering achieved Dice scores of 0.73 for the mediodorsal-parafascicular (MD-Pf) complex and 0.51 for the ventral posterolateral (VPL) nucleus and produce a cluster corresponding to LGN. Furthermore, by combining structural and diffusion information, our approach enabled subdivision of the pulvinar into four distinct regions. These result position our modified spectral clustering as a robust and anatomically informed tool for thalamic clustering and pulvinar sub-segmentation.

Leveraging diffusion tractography, connectivity-based parcellation (CBP) is one of the oldest methods for thalamic nuclei segmentation. The goal of this work was to reassess CBP using higher spatial resolution diffusion MRI data and reconstruction algorithms, and to compare it with recent state-of-the-art methods for thalamic nuclei segmentation. Furthermore, these methods were systematically evaluated against three histological atlases and one functional MRI-based atlas to examine their relative anatomical similarities and differences. High resolution diffusion and T1-weighted MRI data from 67 healthy individuals in the Human Connectome Project Young Adult database were analyzed. CBP was performed using probabilistic tractography with cortical targets derived from combining labels of the Human Connectome Project Multi-Modal Parcellation 1.0 atlas into 8, 11, and 23 regions. Results were compared against three recent methods: orientation distribution function clustering (ODF), track density imaging (TDI), and structural MRI-based segmentation. Group level analyses were conducted in the Montreal Neurological Institute space, and Dice overlap coefficients were calculated using four atlases (three histological, one functional). CBP results using newer data and methods were still remarkably similar to the original CBP parcellation results. Across atlases, a consistent hierarchy was observed: HIPS-THOMAS performed best, followed by TDI, ODF, and CBP (Kendall's W = 1.00, p = 0.007). Histological atlases showed strong mutual agreement (Pearson r = 0.71-0.85), whereas the Zhang atlas demonstrated lower concordance (Pearson r = 0.51-0.63). Despite methodological advances, CBP remains constrained in its ability to delineate thalamic nuclei with histological accuracy. By contrast, structural and diffusion microstructural approaches provided better nuclear localization. These findings highlight the need for hybrid workflows that integrate structural and diffusion-based information to enable more reliable thalamic segmentation for neuroscience research.

Accurate thalamic nuclei segmentation is critical for neuroscience research and clinical interventions such as deep brain stimulation and magnetic resonance guided focused ultrasound. Connectivity based parcellation has been widely used for two decades, yet its anatomical validity remains uncertain compared with newer imaging approaches. Methods:We analyzed high resolution diffusion magnetic resonance imaging (MRI) and T1 weighted data from 67 healthy young adults in the Human Connectome Project. Connectivity based parcellation was performed using probabilistic tractography with cortical targets derived from the HCP MMP1 atlas, generating 8, 11, and 23 region parcellations. Results were compared against three state of the art methods: orientation distribution function (ODF) clustering, track density imaging (TDI), and the structural MRI based segmentation. Group level analyses were conducted in Montreal Neurological Institute and Hospital (MNI) space, and Dice overlap coefficients were calculated against the histology based Morel atlas. Results:Connectivity based parcellation demonstrated limited anatomical precision, with increasing cortical target counts introducing greater variability and noise without improving nuclear boundary definition. ODF clustering and TDI recovered subdivisions consistent with cytoarchitectonic patterns, particularly in the pulvinar and mediodorsal nuclei. Structural MRI based segmentation achieved the highest overall Dice coefficients, closely approximating Morel defined boundaries, while Connectivity based parcellation consistently underperformed across nuclei. Conclusion:Despite methodological advances, Connectivity based parcellation remains constrained in its ability to delineate thalamic nuclei with histological accuracy. By contrast, structural and diffusion microstructural (ODF, TDI) approaches provide superior nuclear localization. These findings highlight the need for hybrid workflows that integrate structural and diffusion based information to enable more reliable thalamic segmentation for research and clinical targeting applications.

Background:Deep grey matter structures such as the thalamus and basal nuclei are implicated in numerous neurological disorders, yet accurate segmentation of these structures from standard T1-weighted MRI remains challenging due to poor intra-subcortical contrast, long preprocessing pipelines, and fragmented toolsets. Methods:We introduce THOMASINA a deep learning pipeline for comprehensive subcortical segmentation from standard T1-weighted (T1w) as well as white-matter-nulled (WMn) MRI. The method leverages labels derived from a recently published state-of-the-art multi-atlas segmentation method to train multiple 3D deep learning-based segmentation models including SwinUNETR, DiNTS, and SegResNet. All networks were trained on cropped volumes and tested on held-out and out-of-distribution datasets. For T1-weighted MRI, an additional synthesis step was used to generate WMn-like contrast prior to segmentation. Results:SegResNet achieved the best performance (mean Dice = 0.89 on with in-domain test data, 0.85 on out-of-domain test data), outperforming DiNTS and SwinUNETR in both accuracy and robustness. It also had the highest mean, median, and minimum Dice and lowest SD in most nuclei compared to the DiNTS and SwinUNETR. Synthetic WMn contrast provided comparable segmentation to actual WMn images. The proposed networks reduced per-subject segmentation time to the order of seconds versus tens of minutes using traditional multi-atlas segmentation. THOMASINA also generalized well across field strengths, scanner vendors, and disease cohorts. Conclusions:THOMASINA offers a fast, reproducible, and scalable solution for comprehensive subcortical segmentation from standard T1w MRI. By combining synthetic WMn contrast with state-of-the-art deep learning-based segmentation models, our method addresses key barriers to deployment and sets a foundation for biomarker discovery in clinical and population-scale imaging studies.

BACKGROUND:Thalamic nuclei facilitate a wide range of complex behaviors, emotions, and cognition and have been implicated in neuropsychiatric disorders including Alzheimer's disease (AD) and schizophrenia (SCZ). The aim of this work was to establish novel normative models of thalamic nuclear volumes and their laterality indices and investigate their changes in SCZ and AD. METHODS:Volumes of bilateral whole thalami and 10 thalamic nuclei were generated from T1 magnetic resonance imaging data using a state-of-the-art novel segmentation method in healthy control participants (n = 2374) and participants with early mild cognitive impairment (n = 211), late mild cognitive impairment (n = 113), AD (n = 88), and SCZ (n = 168). Normative models for each nucleus were generated from healthy control participants while controlling for sex, intracranial volume, and site. Extreme z-score deviations (|z| > 1.96) and z-score distributions were compared across phenotypes. z Scores were associated with clinical descriptors. RESULTS:Increased infranormal and decreased supranormal z scores were observed in SCZ and AD. z Score shifts representing reduced volumes were observed in most nuclei in SCZ and AD, with strong overlap in the bilateral pulvinar, medial dorsal, and centromedian nuclei. Shifts were larger in AD, with evidence of a left-sided preference in early mild cognitive impairment while a predilection for right thalamic nuclei was observed in SCZ. The right medial dorsal nucleus was associated with disorganized thought and daily auditory verbal hallucinations. CONCLUSIONS:In AD, thalamic nuclei are more severely and symmetrically affected, while in SCZ, the right thalamic nuclei are more affected. We highlight the right medial dorsal nucleus, which may mediate multiple symptoms of SCZ and is affected early in the disease course.

Postural instability, a concomitant of falls, can persist in people with alcohol use disorder (AUD) even with sustained sobriety. Balance testing using a force plate, which detects micromovements while standing still, can be quantified with spectral analysis and expressed as temporal frequency, an index of truncal (i.e., postural) tremor. Here, we investigated physiological and brain structural factors that may contribute to a mechanistic understanding of postural instability during quiet standing in AUD. This mixed cross-sectional/longitudinal design included 462 observations in 292 participants (age 25-75 years): 120 men and 44 women with DSM-5-determined AUD and 75 control men and 53 control women. All participants completed balance testing on a force plate under two conditions: eyes open and eyes closed, both with feet together. Most participants also underwent two-point discrimination testing on the soles of the feet and structural MRI, typically within the week of balance testing. Linear mixed-effects models revealed greater tremor in all conditions in the AUD than control group with the diagnostic differences attributed to AUD men. Age effects did not differ significantly between AUD and control groups. By contrast, stronger correlations were detected between greater tremor, measured as a 2-5 Hz/0-2 Hz frequency quotient, and smaller regional brain volumes selective to motor centers (frontal supplemental motor cortex, thalamus, pallidum, cerebellar white matter) of the AUD men. The salient signs of postural instability were attributable to AUD men who consumed alcohol exceeding NIAAA guideline limits in the year prior to testing.

The pulvinar, the largest thalamic nucleus, is a highly interconnected structure supporting perception, visuospatial attention, and emotional processing. Such a central role relies on a precise topographical organization reflected in anatomical connectivity and neurochemical markers. Traditionally subdivided into distinct subnuclei, recent work shows that these divisions only partially explain its organization, which is better captured by continuous gradients of cortical connections along dorso-ventral and medio-lateral axes. While well studied in primates, this gradient-based architecture remains less explored in humans. The present work combines high-quality, multimodal structural and functional imaging with a whole-brain, large-scale, PET atlas mapping 19 neurotransmitter systems. By applying diffusion embedding to tractography, functional connectivity, and receptor coexpression, we identify multiple gradients of structural connections, functional coactivation, and molecular binding patterns. These converge on a shared representation along the dorso-ventral and medio-lateral axes of the human pulvinar, aligning with connectivity transitions from lower-level to higher-order cortical regions. Moreover, this is paralleled by gradual changes in the expression of molecular markers associated with key neuromodulator systems, including serotoninergic, noradrenergic, dopaminergic, and opioid systems. Our findings advance the understanding of pulvinar anatomy and function, offering an exploratory framework to investigate the role of this structure in both health and disease.

There is a lack of tools for comprehensive and complete segmentation of deep grey nuclei using a single software for reproducibility and repeatability. We present a fast, accurate, and robust method for segmentation of deep grey nuclei (thalamic nuclei, basal ganglia, amygdala, claustrum, and red nucleus) from structural T1 MRI data at conventional field strengths. We leveraged the improved contrast of white-matter-nulled imaging by using the recently proposed Histogram-based Polynomial Synthesis (HIPS) to synthesize white-matter nulled images from standard T1 and then use a multi-atlas segmentation with joint label fusion to segment deep grey nuclei. The method worked robustly on all field strengths (1.5/3/7T) and Dice coefficients ≥ 0.7 were achieved for all structures compared against manual segmentation ground truth. In conclusion, this method facilitates careful investigation of deep grey nuclei by enabling the use of conventional T1 data from large public databases, which has not been possible hitherto due to lack of robust reproducible segmentation tools.

INTRODUCTION:Frontotemporal dementia involves progressive atrophy in deep gray matter nuclei, including the thalamus and basal ganglia (such as the caudate, putamen, nucleus accumbens, and globus pallidus), which are critical for cognition and behavior. This study examined cross-sectional and longitudinal atrophy using a state-of-the-art multi-atlas segmentation method sTHOMAS. METHODS:T1-weighted MRI scans from 274 participants at baseline and 237 at follow-up obtained from the Frontotemporal Lobar Degeneration Neuroimaging Initiative database were analyzed using sTHOMAS. Group differences were assessed using ANCOVA, adjusting for age, gender and intracranial volume as covariates. RESULTS:Atrophy was significant in the mediodorsal, pulvinar, anterior ventral nuclei, nucleus accumbens, and claustrum, with bvFTD most affected cross-sectionally. Longitudinally, the nucleus accumbens, mediodorsal, and pulvinar nuclei declined further. Atrophy correlated with naming (mediodorsal), working memory (ventrolateral posterior), and executive dysfunction (nucleus accumbens) neuropsychological tests. DISCUSSION:These findings highlight progressive, nucleus-specific atrophy in FTD and emphasize the importance of cross-sectional as well as longitudinal imaging and sex-specific analyses in understanding disease progression.

The utility of neuroimaging in the diagnosis and management of movement disorders has been steadily increasing as both imaging and image analysis technologies have advanced in the last decade. Neuroimaging is also playing a critical role in the search for novel therapies to prevent, slow down, and treat various movement disorders. This article reviews both standard and innovative imaging tools available for both clinicians and researchers. We focus predominantly on the clinician's perspective, discussing imaging tools that are becoming rapidly available and how these may be integrated into the clinic to provide cutting-edge and patient-centered care. We discuss novel and emerging techniques and their potential implications for the field, as well as highlight areas still in need of imaging solutions.

BackgroundWhile cortical atrophy has been widely studied in dementia, emerging evidence highlights the role of subcortical degeneration, particularly in deep gray matter structures such as the thalamus, claustrum, and basal nuclei, in both Alzheimer's disease (AD) and frontotemporal dementia (FTD). However, disease-specific subcortical patterns remain undercharacterized.ObjectiveTo quantify deep gray matter atrophy across the AD continuum (mild cognitive impairment and AD) and three FTD subtypes (bvFTD, svFTD, PNFA), and to assess longitudinal atrophy, cognitive associations, and diagnostic classification.MethodsWe applied a novel segmentation pipeline (sTHOMAS) to T1-weighted MRI data from 380 participants in the ADNI cohort and 274 participants in the FTLDNI cohort, with longitudinal follow-up available for 237 participants. Group differences were assessed using ANCOVA (adjusted for age and sex), followed by post hoc comparisons and effect size estimation (Cohen's d). Neuropsychological associations were examined using partial correlations. A hierarchical Random Forest model was trained to classify diagnostic groups.ResultsPronounced atrophy was observed in the mediodorsal, anteroventral, pulvinar thalamic nuclei, and nucleus accumbens, and claustrum in both AD and FTD, but was significantly greater in bvFTD. Longitudinal analysis revealed the fastest progression in bvFTD. Classification achieved 96.8% accuracy (AUC = 0.99) for AD versus FTD and 77.7% accuracy (AUC = 0.83) for PNFA versus svFTD. Subcortical atrophy correlated to executive, language, and semantic deficits.ConclusionsAtrophy in the mediodorsal, pulvinar, anteroventral thalamic nuclei, nucleus accumbens, and claustrum distinguishes AD from FTD and differentiates FTD subtypes. These subcortical structures represent promising biomarkers for diagnosis and monitoring of neurodegeneration.

BACKGROUND:Midline orofacial and brain structures, including the multinucleated thalamus, may be differentially sensitive to prenatal alcohol exposure and vulnerable to accelerated aging. METHODS:Two sets of magnetic resonance imaging (MRI) data separated by 20 years are reported for control individuals, individuals with fetal alcohol syndrome (FAS), and nondysmorphic individuals with heavy fetal alcohol exposure (FAE). MRI1 included 179 participants, with 69 participants reassessed at MRI2. Segmentation produced estimates of bilateral thalamic volume and 10 bilateral nuclei, which were aggregated into anterior, ventral, posterior, and medial volumes. Differences were assessed with and without correction for intracranial volume (ICV). RESULTS:MRI1 revealed stepwise group differences in ICV, total thalamic volume, and anterior and ventral regions uncorrected for ICV, where control > FAE > FAS. Corrected for ICV, the smaller volumes persisted in the anterior and ventral regions, although differences between the FAE and FAS groups were attenuated. Nuclei volumes were selectively smaller in the alcohol-exposed groups than in the control group even after controlling for ICV. Longitudinally, thalamic volumes typically declined over time, maintaining the stepwise effects and with little evidence for accelerated decline in the FAE or FAS groups. CONCLUSIONS:These novel data revealed stable deficits in thalamic nuclei of the groups with heavy prenatal alcohol exposure. After 20 years, the deficits persisted but without accelerated age-related decline and following the same aging pattern as control individuals. Despite parallel aging functions in all groups, ICV adjustment yielded volume deficits localized to the anterior and ventral thalamic nuclei, differing from patterns in the remaining thalamic nuclei and cortical brain structures.

The thalamus and its constituent nuclei are critical for a broad range of cognitive, linguistic, and sensorimotor processes, and are implicated in many neurological and neurodegenerative conditions. However, the functional involvement and specificity of thalamic nuclei in human neuroimaging work is underappreciated and not well studied due, in part, to technical challenges of accurately identifying and segmenting nuclei. This challenge is further exacerbated by a lack of common nomenclature for comparing segmentation methods. Here, we use data from healthy young (Human Connectome Project, n = 100) and older healthy adults, plus those with mild cognitive impairment and Alzheimer's disease (Alzheimer's Disease Neuroimaging Initiative, n = 540), to benchmark four state-of-the-art thalamic segmentation methods for T1 MRI (FreeSurfer, histogram-based polynomial synthesis [HIPS]-THOMAS, synthesized contrast segmentation [SCS]-convolutional neural network [CNN], and T1-THOMAS) under a single segmentation framework. Segmentations were compared using overlap and dissimilarity metrics to the Morel stereotaxic atlas, a widely accepted thalamic atlas. We also quantified each method's estimation of thalamic nuclear degeneration across Alzheimer's disease progression, and how accurately early and late mild cognitive impairment, and Alzheimer's disease could be distinguished from healthy controls. We show that the HIPS-THOMAS approach produced the most effective segmentations of individual thalamic nuclei relative to the Morel atlas, and was also most accurate in discriminating healthy controls from those with mild cognitive impairment and Alzheimer's disease using individual nucleus volumes. This latter result was different when using whole thalamus volumes, where the SCS-CNN approach was the most accurate in classifying healthy controls. This work is the first to systematically compare the efficacy of anatomical thalamic segmentation approaches under a unified nomenclature. We also provide recommendations of which segmentation method to use for studying the functional relevance of specific thalamic nuclei, based on their overlap and dissimilarity with the Morel atlas.

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.

Objective: Pharmacologically-treated people living with HIV infection have near-normal life spans with more than 50 % living into at-risk age for dementia and a disproportionate number relative to uninfected people engaging in unhealthy drinking. Accelerated aging in HIV occurs in some brain structures including the multinucleated thalamus. Unknown is whether aging with HIV affects thalamic nuclei and associated functions differentially and whether the common comorbidity of alcohol use disorder (AUD) + HIV accelerates aging. Methods: This mixed cross-sectional/longitudinal design examined 216 control, 69 HIV, and 74 HIV + AUD participants, age 25-75 years old at initial visit, examined 1-8 times. MRI thalamic volumetry, parcellated using THalamus Optimized Multi-Atlas Segmentation (THOMAS), identified 10 nuclei grouped into 4 functional regions for correlation with age and measures of neuropsychological, clinical, and hematological status. Results: Aging in the control group was best modeled with quadratic functions in the Anterior and Ventral regions and with linear functions in the Medial and Posterior regions. Relative to controls, age-related decline was even steeper in the Anterior and Ventral regions of the HIV group and in the Anterior region of the comorbid group. Anterior volumes of each HIV group declined significantly faster after age 50 (HIV = -2.4 %/year; HIV + AUD = -2.8 %/year) than that of controls (-1.8 %/year). Anterior and Ventral volumes were significantly smaller in the HIV + AUD than HIV-only group when controlling for infection factors. Although compared with controls HIV + AUD declined faster than HIV alone, the two HIV groups did not differ significantly from each other in aging rates. Declining Attention/Working Memory and Motor Skills performance correlated with Anterior and Posterior volume declines in the HIV + AUD group. Conclusions: Regional thalamic volumetry detected normal aging declines, differential and accelerated volume losses in HIV, relations between age-related nuclear and performance declines, and exacerbation of volume declines in comorbid AUD contributing to functional deficits.

BackgroundPrevious studies have shown that thalamic and hippocampal neurodegeneration is associated with clinical decline in Multiple Sclerosis (MS). However, contributions of the specific thalamic nuclei and hippocampal subfields require further examination.ObjectiveUsing 7 Tesla (7T) magnetic resonance imaging (MRI), we investigated the cross-sectional associations between functionally grouped thalamic nuclei and hippocampal subfields volumes and T1 relaxation times (T1-RT) and subsequent clinical outcomes in MS.MethodsHigh-resolution T1-weighted and T2-weighted images were acquired at 7T (n=31), preprocessed, and segmented using the Thalamus Optimized Multi Atlas Segmentation (THOMAS, for thalamic nuclei) and the Automatic Segmentation of Hippocampal Subfields (ASHS, for hippocampal subfields) packages. We calculated Pearson correlations between hippocampal subfields and thalamic nuclei volumes and T1-RT and subsequent multi-modal rater-determined and patient-reported clinical outcomes (∼2.5 years after imaging acquisition), correcting for confounders and multiple tests.ResultsSmaller volume bilaterally in the anterior thalamus region correlated with worse performance in gait function, as measured by the Patient Determined Disease Steps (PDDS). Additionally, larger volume in most functional groups of thalamic nuclei correlated with better visual information processing and cognitive function, as measured by the Symbol Digit Modalities Test (SDMT). In bilateral medial and left posterior thalamic regions, there was an inverse association between volumes and T1-RT, potentially indicating higher tissue degeneration in these regions. We also observed marginal associations between the right hippocampal subfields (both volumes and T1-RT) and subsequent clinical outcomes, though they did not survive correction for multiple testing.ConclusionUltrahigh field MRI identified markers of structural damage in the thalamic nuclei associated with subsequently worse clinical outcomes in individuals with MS. Longitudinal studies will enable better understanding of the role of microstructural integrity in these brain regions in influencing MS outcomes.