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Thalamic nuclei have been implicated in neurodegenerative and neuropsychiatric disorders. Normative models for thalamic nuclear volumes have not been proposed thus far. The aim of this work was to establish normative models of thalamic nuclear volumes and subsequently investigate changes in thalamic nuclei in cognitive and psychiatric disorders. Volumes of the bilateral thalami and 12 nuclear regions were generated from T1 MRI data using a novel segmentation method (HIPS-THOMAS) in healthy control subjects (n=2374) and non-control subjects (n=695) with early and late mild cognitive impairment (EMCI, LMCI), Alzheimer's disease (AD), Early psychosis and Schizophrenia, Bipolar disorder, and Attention deficit hyperactivity disorder. Three different normative modelling methods were evaluated while controlling for sex, intracranial volume, and site. Z-scores and extreme z-score deviations were calculated and compared across phenotypes. GAMLSS models performed the best. Statistically significant shifts in z-score distributions consistent with atrophy were observed for most phenotypes. Shifts of progressively increasing magnitude were observed bilaterally from EMCI to AD with larger shifts in the left thalamic regions. Heterogeneous shifts were observed in psychiatric diagnoses with a predilection for the right thalamic regions. Here we present the first normative models of thalamic nuclear volumes and highlight their utility in evaluating heterogenous disorders such as Schizophrenia., Competing Interests: Conflict of Interest Neither the authors or their institutions received any payments or services in the past 36 months from a third party that could be perceived to influence, or give the appearance of potentially influencing, the submitted work

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.